dataesr/scientific-paragraphs-categorization huggingface dataset:
5 percentiles are reported. To assess estimation bias in the parameters of Pradel models, two approaches were used. 100 datasets per taxon were resampled with replacement, encounter histories constructed and the minimum adequate model fitted to each of these pseudo-datasets. The year-dependent covariates were not resampled. I report estimates for survival probabilities and colonization corrected with bootstrap estimates of bias. The Supplementary Material presents results of further simulations of local survival, colonization, temporary emigration and sampling, which allowed another assessment of estimation bias and of the statistical power to detect decelerating declines and temporary emigration concentrated in a certain period.
A decelerating decrease of species richness occurs when (1) there is a decline in the first half of the interval and (2) any straight line with negative slope drawn within the confidence band in the first half of the study period, runs below the confidence band for the second half. There is no decelerating decrease of species richness in the models for non-Bombus genera. The maximal model fitted to the full dataset suggests that it occurs for Bombus (Fig. 2 ). In the maximal models for Bombus richness does fluctuate over time, with decreases as good as stalling in the first half of the study period as well, so that it is unclear if any deceleration is real and persists. Comparison with the analyses on the data subset for grids that were repeatedly sampled reveals that a decelerating decline is not found in the adequate model there, and that the richness of non-Bombus species did not decline.
Capture-recapture analysis Figures three and four show the results of fitting time-dependent (left, no mixtures) or regression Pradel models with mixtures (right) to the data. Reducing the dataset to a shorter interval  showed in time-dependent models that colonization was again increased at the new beginning of the shorter interval and that survival decreased near the new end, revealing that the similar estimated survival and colonization patterns near the start and the end of the entire study period are to be seen as biased. In both groups of species, models with detection probabilities that are mixtures of several components were preferred (Table  1 , Figures  3  and 4 ). The time-dependent models show negligible colonization and years with reduced survival. In the case of Bombus, the absence of a year with decreased survival between 1985 and 2000 makes the species richness decline decelerate in the time-dependent model: there just isn't a downward step in that period (Figure  4 , left column).
Discussion A dataset with records and observations of wild bee species in the Netherlands was analysed using different approaches to investigate trends in species richness for Bombus and non-Bombus genera across almost 70 years. Neither of these approaches detected a decelerating trend with sufficient consistency to reject the hypothesis of a constant decline or no decline at all. Generalized non-linear modelling showed potential estimation bias for non-Bombus genera, which followed directly from limitations on the bootstrap procedure, as absent species can not be resampled. I therefore take this bias rather as an indication of the importance of species with few records in the results. Simulations indicated that the modelling of species richness changes via encounter histories suffered from estimation bias of parameters representing time trends, making it difficult to detect decelerating declines using that method altogether.
There is thus no reason to be satisfied with current biodiversity conservation efforts, as there is no or insufficient evidence that they have been effective. where the fraction of rare missing species is first estimated and then added to an imputed dataset from which resampling occurs. I have used bootstrap methods differently, to estimate magnitudes of estimation bias in the different types of analysis. The idea there is that the estimates based on the resamples differ from the estimate on the actual data in the same way as the estimate differs from the true value  (Davison & Hinkley 1997). The approach found estimation bias in non-linear regressions for non-Bombus. It should be further verified whether bias was adequately estimated using this bootstrap or just a side effect of the importance of species with few records in a non-random sample.
For the capture-recapture analysis, bootstrap bias estimates did not very well align with expectations from simulations. Issues with models for encounter histories will probably be alleviated when robust design models  (Pollock 1982 ) can be fitted to the data. If records had been collected throughout the study period independently by observations or via specimens deposited in collections, then these two sampling methods could have been used to fit a robust design. The pattern of estimation bias we found might also be specific to this dataset. Heterogeneity in species detection probabilities can be expected in all methods addressing species richness (e. g. Boulinier et al. 1998 ) and it was incorporated into the models by means of mixtures. Note that models for open populations were used in the analysis of species encounter histories.
Generalized non-linear regression This approach and some of its characteristics are detailed in the main text. None of the estimation methods proposed by  Raaijmakers (1987)  nor the non-linear Least Squares used by O'Hara (2005) were applied. Here I paste the code for one of the most elaborate models fitted using gnlr  (Lindsey 1997) , including the initial values for all parameters used in the ML optimization. In the short script below, "Sp" is the number of species, "spl8" is a natural cubic spline of eight d. f. of the year variable, "rec" is the number of records per year. "SD" is the predicted unconditional standard deviation of the number of species at the actual number of records, calculated following  Colwell et al. (2012). gnlr(sp,mu=~exp(a+a1*spl8[,1]+a2*spl8[,2]+a3*spl8[,3]+a4*spl8[,4]+a5*spl8[,5]+a6*spl8[, 6]+a7*spl8[,7]+a8*spl8[,8])*rec/(rec+exp(b+b1*spl8[,1]+b2*spl8[,2]+b3*spl8[,3]+b4*spl8, 4]+b5*spl8[,5]+b6*spl8[,6]+b7*spl8[,7]+b8*spl8[,8])), pmu=c(1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0), shape=~log(exp(v0)+SD^2),pshape=list(v0=-2)) Full scripts with all models and the model selection procedures can be provided as text files.
Smooth regressions of species richness estimates In this third type of analysis, I used species richness estimates in smooth regression models with several covariates. As estimates of species richness, I used the "best" estimator selected among five different parametric models using a detailed heuristic  (Bunge et al. 2012 ). This approach thus does not have a clear part of the model representing the observation process, except for the fact that some covariates related to it are used in smooth regressions. Program CatchAll  (Bunge et al. 2012 ) was used to obtain these estimates of species richness per year, with an estimated standard deviation. Time patterns in log annual species richness estimates were then modelled using smooth functions of the following explanatory variables: the year where the samples or records were from, the number of 10x10 km grid cells with records per year, and the estimated variance parameter  of the Poisson lognormal distribution fitted to numbers of records per species per year for the taxonomic group analysed.
These smooth regressions of log-transformed species richness were fitted using the gamlss() function for R  (Stasinopoulos and Rigby 2007). I modelled heterogeneous errors in log species richness. I fitted as maximal models cubic splines of 9 d. f. for the year variable, and of 4 d. f. for the other explanatory variables (note that d. f. in gamlss() output are given as on top of the linear model). First the model was simplified by reducing the d. f. of the cubic spline for the year variable, subsequently by doing that for the other variables. Models were compared using AICc as for the models in the main text, until a minimum adequate model with lowest AICc was identified. For the estimation bias assessments in this analysis, 100 datasets were re-sampled per year and per taxonomic group and the best species richness estimate determined for each resample.
formula=~offset(exp(predsecorr/predcorr))) Figure  S1  Results when modelling different subsets separately. Results of generalized non-linear models and smooth regressions. Analysis of the full datasets in the left column. Non-Bombus and Bombus records for which specimens were deposited in museum collections (middle), for grid cells sampled in each of the three time periods (right). Estimates of species richness (gnlr) and of the relative change in species richness across years (gamlss). Selected models are given in Table  S1  below. Table  S1. Adequate models obtained for the gnlr and gamlss analyses of data subsets. For gnlr, the explanatory variable and the degrees of freedom of the splines used to model a() and b() are given. For gamlss the spline degrees of freedom of the variables in the selected regression models.
To simulate survival, we drew per year and species a random binary variable with average probability of success (survival) of (S1) With year for the year regression effect and with x ra for the relative abundance of the species in that year. Parameter values for  0 were (4, 5, 6, 7), for  1 (-0. 02, 0, 0. 02, 0. 05), for  2 (0, 0. 0005, 0. 0010). Per combination of parameter values, three replicates were simulated and simulations were repeated for Bombus and non-Bombus genera separately, with similar results. To each simulated dataset, different Pradel models were fitted. All estimated a constant colonization probability. Local survival was either constant, with a regression of the year effect, with an effect of the total number of records per species, with additive effects of both. Detection probabilities were fitted with a regression of the year effect, with effects of  and the number of observations, with the last two effects and the total number of records.
O'Hara (2005)or that. Retrospective sampling standardization is impossible. With data of the type analysed here, we therefore need to resort to detailed statistical modelling, with conservative inference to avoid new erroneous conclusions. The situation could have been as bad asO'Hara (2005)suggested, namely that we often rather analyse properties of estimators than richness patterns themselves. This is confirmed for the estimation of time trends in local survival using Pradel models, where estimation bias leads to wrong inference and masks differences between datasets. The relatively simple generalized non-linear model thus becomes the most dependable tool to infer species richness trends for now. It does not need to assume constant temporary emigration, and the manner in which the sampling process is modelled comes with limited assumptions.
Bombus Bumblebees -Time trends for each subset analysis Generalized linear models fitted to grid cells that were not repeatedly sampled in different time periods. When generalized linear models are used to analyse the dataset restricted to grid cells that were sampled early, middle and late in the study period, a decelerating decline is not retained in the minimum adequate model. To investigate whether the decelerating decline in the full dataset is due to grid cells that were not repeatedly sampled across the study period, these are analysed separately as well. Here I report on the analysis of the dataset consisting of records from grids that were not in the subset presented in Figure two (rows two and four). These data consist of 162492 records of non-Bombus bees, and 85539 records of Bombus bumblebees.
RESULTS Most deregulated mRNAs are common between FA and FTC 20 FA and 8 FTC were hybridized onto double channel microarrays (HEEBO), alongside their normal adjacent thyroid tissue (clinical data, Supplementary Table  1 ). We first assessed the mutational status of the most common reported genetic alterations, finding few mutated samples (Supplementary Table  1 ). We next assessed global expression differences between FA and FTC, that is extensive differences detectable when all the genes present on the arrays were considered. An MDS analysis was performed and did not allow us to clearly distinguish both tumor types (Figure  1A ). Similar results were obtained when using published FA-FTC datasets (Supplementary Figure  1 ). Thus FA and FTC have similar expression profiles when compared on a global scale.
Deregulated pathways are partially shared between FA and FTC The genes deregulated in FA and FTC were submitted to a Pathway Analysis (David Database)  [16] , revealing enriched gene ontologies and pathways shared between FA and FTC (Table  1 ). For biological processes, categories such as cell adhesion, extracellular matrix organization and blood vessel morphogenesis were enriched in both tumor types, while some additional categories were only enriched in FTC, such as angiogenesis and cell migration. Most molecular functions and KEGG pathways were also common to both tumor types, with no deregulations specific for FA, but additional categories specific in FTC such as lipid transporter activity, focal adhesion and ECM receptor interaction. In addition, analysis of genes that were deregulated in FTC compared to normal tissues, utilizing the PANTHER database, revealed a recurrent enrichment of the Gene Ontology category LIPID TRANSPORT (GO: 0006869) with both types of microarray studies (HEEBO and Affymetrix enrichment fold 2.
Our differentially expressed genes are regulated in other FA and FTC datasets, and conversely We considered the identified differentially expressed genes between FA and FTC (fold change ≥ |2|) as a gene set and evaluated their collective expression with GSEA in the publicly available datasets of other studies from Borup et al. , Finley et al. , Weber et al. , and Giordano et al. [17] [18] [19] [20]. Collectively, these genes were significantly regulated in the same direction in the Finley data set (q value = 0. 066, NES = -1. 371), and the Giordano dataset (q value = 0. 0101, NES = -1. 527), and were in line (though not statistically significant) with the Borup dataset (q value = 0. 157, NES = -1. 316), and the Weber data set (q value = 0. 327, NES = -1. 170) (Supplementary Figure  2A ). These results showed that our signature is not restricted to our data set but is consistent with other reported works.
MiRNA analysis revealed few deregulated miRNA in FTC and fewer in FA To further analyze the molecular phenotype of follicular tumors, miRNA expression profiling was performed on 10 FA and 9 FTC. The tumor samples and their normal adjacent tissues were hybridized onto inhouse printed microarrays covering 841 human miRNA. SAM analyses were used to obtain lists of deregulated miRNAs (q-val ≤ 5% and fold change ≥|1. 5|) (Table  3A  and 3B ). A multidimensional scaling analysis performed with all the miRNA expression data showed a good separation between FA and FTC with the exception of 3 samples: FTC 139, FA 131, FA 137 (Figure  5 ). Among these, FA 131 and FA 137 mixed with the FTCs had no particular clinical features that would justify their exclusion and were kept for further analyses. On the other hand FTC 139 presented an important inflammatory reaction (thyroiditis) known to provoke large transcriptomic perturbations  [21].
2. 86E-03 David database pathway analysis of the genes regulated with a fold change ≥ |2|, and a q value ≤ 5%, in FA and in FTC. Common deregulated Gene ontology categories and KEGG Patways for FA and FTC, and categories/pathways restricted to FTC (bold, underlined). from benign to malignant tumors (as already suggested by others)  [38, 39]. This is reflected in our microarray data which shows a large proportion of common deregulations between FA and FTC. Furthermore, most of the mRNA differentially regulated between FA and FTC were modulated in the same direction, with the extent of modulation stronger in FTC. In addition, both types of tumor share the same perturbations of signaling pathways. Of course, FTC, as more advanced tumors, display additional dysregulated genes and pathways. One of the most striking differences between FA and FTC is invasion and migration; this is reflected in our gene expression data, where genes associated with cell migration, focal adhesion, and ECM-receptor interaction are specifically highlighted in the carcinomas.
MATERIALS AND METHODS Samples 20 follicular non-autonomous adenomas and 12 follicular carcinomas (minimally and widely invasive) were obtained from multiple clinics: Pitié-Salpêtrière (Paris, France), Jules Bordet Institute (Brussels, Belgium), CHU Angers (Angers France), Hôpital de Jolimont (La Louvière, Belgium). Diagnoses were confirmed by thorough microscopic examination of the surgical samples by the respective pathologists of the different Institutions. Tissues were immediately dissected, placed on ice, snap-frozen in liquid nitrogen and stored at -80°C until RNA processing. Protocols have been approved by the ethics committees of the Institutions. The available patient information, clinical and gene alteration data relative to these samples are presented in Supplementary Table  1. Some samples have been analysed both for mRNA and miRNA profiles, and some FTC were analysed both on HEEBO (dual channel) slides and Affymetrix.
Data acquisition and bioinformatic analyses Double channel slides were scanned using a Molecular Devices 4000B laser scanner and expression levels were quantified using GenePix Pro 6. 1 image analysis software (Axon Instruments, CA, USA). For mRNA hybridization, data analyses were performed using BRB-ArrayTools  [77]. Data were imported using the GenePix data importer, ratios were flipped for reverse-fluor experiments, and background adjustment was applied. Both red and green spots with intensities below the minimum and flagged spots were excluded from the analysis. Data were normalized using lowesssmoother (locally weighted scatterplot smoothing). For miRNA hybridizations, R tools were used as described previously  [64]. Regulated genes were selected using significant analysis of microarrays (SAM)  [78] , and data were visualized using the R software version 2.
11. 1  [79]  or GenePattern (http://www. broad. mit. edu/cancer/software/genepattern/). This second algorithm was also used for normalization of Affymetrix data (CELL files GCRMA) and other applications such as class prediction based on leave-one-out cross-validation that was performed with the k-nearest neighbours algorithm (KNNXValidation)  [80]. Gene Set Enrichment Analysis (GSEA; MsigDB) was used to search for multigene signatures allowing to distinguish classes  [81]. Biological significance of regulations were conducted with the DAVID (Database for Annotation, Visualisation and Integrated Discover) software  [16]  and PANTHER database http:// pantherdb. org [82].
4. 5. 2. 3 Operating instructions 1 -Sample of 3 to 6 grams should be studied in order they include at least 2000 identified particles that cover more than 100 pixels (i. e. : with an area greater than 0. 18 mm 2 at 600 dpi resolution) 2 -A commonly available flatbed scanner can be used (it is cheap and convenient). It avoids image distortion and ensures repetitive calibration. 8-bit gray scale image with 600 dpi (dot per inch) definition appears to be a good compromise. 3 -Use a dark background in order to obtain the maximum contrast. Particles can be laid and stuck on a black sheet using a glue spray, or directly laid on the glass of scanner. Spread the particles in a manner that they do not touch or overlap one another. 4 -Use ImageJ (or equivalent software) to analyse the images. (NB guidelines and user manual are available online).
, 2009a, c, Emission  Database for Global Atmospheric Research) database, distributed vertically to account for different injection altitudes  (Pozzer et al. , 2009). Bulk natural aerosol emissions (i. e. , desert dust and sea spray), are treated using offline monthly emissions files based on AEROCOM  (Dentener et al. , 2006)  and hence are independent of the meteorological conditions. The atmospheric chemistry is simulated with the MECCA (Module Efficiently Calculating the Chemistry of the Atmosphere) submodel by  Sander et al. (2005 Sander et al. ( , 2011)) , and the aerosol microphysics and gas-aerosol partitioning are calculated by the Global Modalaerosol eXtension (GMXe) aerosol module  (Pringle et al. , 2010a, b). Gas/aerosol partitioning is calculated using the ISORROPIA-II model  (Fountoukis and Nenes, 2007; Nenes et al.
, 1998a, b). Following the approach of  Pozzer et al. (2012b) , the year 2010 is used as the reference year, the feedback between chemistry and dynamics was switched off, and therefore all simulations described here are based on the same (binary identical) dynamics and consequent transport of tracers. Although  Pozzer et al. (2012a)  evaluated the model for the same configuration and emissions database, the emissions referred to the year 2005, while here the emissions for the year 2010 are used. Therefore the model is re-evaluated for the species of interest (i. e. , SO 2- 4 , NO - 3 , NH + 4 and PM 2. 5 ). The model results of this study have been evaluated against satellite based PM 2. 5 estimates  (van Donkelaar et al. , 2010) ; the results are shown in Fig. 2  and are summarized in Table  1 , also focusing on the four regions focus of this study (i.
e. , North America, Europe, South and East Asia). Compared to global satellite-derived PM 2. 5 concentrations this model version, with prescribed dust emissions, consistently overestimates PM 2. 5 over desert areas (see Fig. 2 ). However, the average concentration of PM 2. 5 at the surface in the regions of interest is within 30 % of the observations. For Europe and South Asia, 95 % of the simulated PM 2. 5 concentrations are within a factor of 2 of the observations, while for North America and East Asia this is about 80 %. Further, SO 2- 4 , NO - 3 and NH + 4 have been compared with station observations from different databases, such as from EPA (United States Environmental Protection Agency), EMEP (European Monitoring and Evaluation Programme)   Jöckel et al. , 2006). As the nitrate concentrations seem to be on the high end of the observations, it must be acknowledged that the effect of reducing ammonia emissions from agriculture could be overestimated.
Therefore, the ammonia emissions used in this work have been compared to other used databases, such as EDGARv4. 3. 1 (Emission Database for Global Atmospheric Research,  Crippa et al. , 2016)  and RCP85 (Representative Concentration Pathways van  Vuuren et al. , 2011b, a). These data sets differ globally by ∼ 15 % (40. 26, 47. 49 and 40. 62 Tg yr -1 for EDGAR-CIRCE, EDGARv4. 3. 1 and RCP85). This reflects the uncertainties in the emission estimates of ammonia, which could be up to 50 % on a local scale  (Beusen et al. , 2008). The implementation of bidirectional exchange of ammonia between the soil and atmosphere may improve the emissions from livestock, although this approach is still associated with underestimates of emissions  (Zhu et al. , 2015). Further, ammonia emitted from traffic is included (∼ 1 % of total ammonia emissions), although toward the lower end of what has been estimated by  Sun et al.