dataesr/scientific-paragraphs-categorization huggingface dataset:
The ensemble statistic gsi T (weighted sum of gsi across genealogies) for each dataset (100 bootstrap trees for each locus and concensus trees) are listed in Table  5. Relatively low gsi value was obtained from the analysis of β-tubulin dataset in A. hordei and ID dataset in A. domesticus, however even in these cases the p-value of the permutation test is nearly 0. The gsi statistic was not calculated for A. infrequens and A. pseudogracilis because they were both represented by only one isolate.
Species tree The species tree topology was inferred with *BEAST  (Heled & Drummond 2010)  and is depicted on Figs 5 and 6. It was used as a guide tree for species validation in BP&P but it also represents the most probable evolutionary relationships between the species. Four clusters of species designated as the  A. restrictus, A. conicus, A. vitricola and A. penicillioides clades are denoted in Fig. 5. We observed only slight differences in the topology of the species tree and statistical support of some nodes inferred with *BEAST when compared to the results of ML and BI analysis of the concatenated and partitioned dataset (see below). Several nodes had only limited support in all types of analyses and there is definitely some degree of uncertainty. These uncertainties can be visualized in DensiTree  (Bouckaert 2010 ) (Fig.
6 ), that displays all trees created during the analysis except burn-in phase and trees with one of the three most common topologies are differently coloured. The most obvious problem is the position of A. halophilicus. Based on the available sequence data it is not clear whether it is situated basally within sect. Restricti or if it is rather phylogenetically related to some clade within the section. In addition, *BEAST analysis supported (PP = 1. 00) an A. conicus clade consisting of six species, A. conicus, A. domesticus, A. destruens, A. villosus, A. gracilis and A. pseudogracilis, however this arrangement was not supported by the concatenated dataset analysis (see Fig. 7 ) where A. gracilis and A. pseudogracilis formed a distinct clade from the remaining four species. The position of A.
Bayesian and Maximum likelihood analysis of the partitioned sequence data All species delimited by methods based on coalescent model were clearly supported by BI and ML analyses (Fig. 7 ). All clades representing species were supported by PP = 1. 00 in BI analysis and at least 93 % bootstrap support in ML analysis. Aspergillus halophilicus was in a polytomy on the base of the section in agreement with the *BEAST analysis. Slight differences were observed in the interspecies relationships between core species of A. penicillioides as mentioned above. The ML and BI analysis of partitioned dataset did not separate A. restrictus and A. conicus clades as did the *BEAST analysis (compare Figs  5  and 7 ).
Comparisons with 454 data A total of 1061 454-pyrosequences were found to belong to sect. Restricti and included in a dataset with 188 ITS barcodes generated during this study. The aligned dataset was 402 bp long and the calculated neighbour-joining tree is shown in Fig. 8. Many species were detected from dust samples, while A. pseudogracilis, A. villosus, A. destruens, A. glabripes, A. salinicola and A. infrequens were not detected. The phylogeny revealed 3-5 unnamed lineages from dust collected from Indonesia, Mexico, Micronesia, the Netherlands, South Africa, Thailand and Uruguay. Two singletons were also detected across the tree. Interestingly, each lineage is represented by OTU's from multiple countries, meaning they are probably common species.
2012). Even though working with species with no infraspecific variability is problematic, it is a general practice to include also these species into the analyses because the authors always want to cover all known variability  (Ahrens et al. 2016). The results of some phylogenetic methods may be biased due to the presence of species with low or no variability, and population genetic methods cannot be used at all with datasets containing species without infraspecific variability. Since the aim of taxonomic revisions and monographs is to describe all current knowledge we included all species irrespective of the number of isolates and presence or absence of infraspecific variability. When we re-inspect the results of our phylogenetic analyses, it is common that the unresolved nodes are occupied by those species represented by a few isolates.
2004 , Kaarakainen et al. 2009 , Amend et al. 2010 , Visagie et al. 2014). Eleven species which were isolated from indoor air are included in our study, namely A. caesiellus, A. conicus, A. destruens, A. domesticus, A. glabripes, A. magnivesiculatus, A. pachycaulis, A. penicillioides, A. restrictus, A. reticulatus and A. tardicrescens. Examples of sect. Restricti members growing in various conditions, including indoor environment and textile can be found in Fig. 36. In a world-wide survey of fungi occurring in house dust using a portion of the ID locus as used here  (Amend et al. 2010) , A. penicillioides was a common OTU identified. In these analyses a 97 % cutoff is most commonly used, however, the low amount of sequence data available for sect. Restricti before this study makes the analysis of species diversity complicated.
Restricti seems to be low considering the small number of reported cases of opportunistic human infections. Several records of human infection caused by A. penicillioides and A. restrictus is summarised by  de Hoog et al. (2009) , but the identification of the pathogens was performed exclusively by morphological examination. It is very likely that some records represent confusion with A. fumigatus due to similar sporulation colour and micromorphology. However, a confirmed case of invasive aspergillosis in human has been described recently that was probably caused by an undescribed species from the A. penicillioides clade (ITS GenBank: KP316198)  (Gupta et al. 2016). This case demonstrated that athough the species of the sect. Restricti are rarely found as pathogens, they have at least limited pathogenic potential.
ACKNOWLEDGEMENTS This research was supported by the the project of the  Charles University Grant Agency  ( GAUK 1434217 ) and the project  BIOCEV  ( CZ. 1. 05/1. 1. 00/02. 0109 )provided by the  Ministry of Education, Youth and Sports of CR  and ERDF. Microbiological and structural investigations of biologically damaged textiles from Slovenian museums was financed by  Slovenian Research Agency  ( J7-4208 ). Cobus M. Visagie ,  Neriman Yilmaz ,  Keith A. Seifert ,  Amanda Chen ,  Robert A, Samson  and  Jos Houbraken  received financial support from the  Alfred P. Sloan foundation Program on the Microbiology of the Built Environment. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  US Department of Agriculture.
A. vitricola clade 0. 02 Fig Fig. 1. A 90 % majority consensus tree of the subgenus Aspergillus inferred with Maximum likelihood analysis based on benA, CaM and RPB2 loci (partitioning scheme and substitution models are listed in Table3). The data set contained 102 strains and 1902 characters, of which 929 characters were variable and 864 were parsimony informative. Support values represent maximum likelihood bootstrap/ Bayesian posterior probability values, 100 % bootstrap values and 1. 00 posterior probability are designated by asterisk. 2. Maximum likelihood phylogenetic tree of the subgenus Aspergillus inferred from partitioned analysis of concatenated dataset (benA, CaM and RPB2) with Maximum likelihood method and presented in radial format. *. The ex-type isolates are designated by a superscript T.
65 = DTO 093-H3 = ATCC 11905 = IMI 172278 = CCF 5447 = IBT 34620]. Fig. 10. Results of linear discriminant analysis (LDA) performed in R 3. 3. 1 based on micromorphological measurements of species from A. restrictus, A. conicus and A. vitricola clade. Dashed ellipses represent 95 % confidence interval, full circles euclidean distances. Analyses involving only two species (B, D, F) are represented by probability density function. 1 -A. restrictus; 2 -A. caesiellus; 3 -A. pachycaulis; 4 -A. conicus; 5 -A. domesticus; 6 -A. destruens; 7 -A. villosus; 8 -A. gracilis; 9 -A. pseudogracilis; 10 -A. vitricola; 11 -A. glabripes. 14 d: flat; colony surface filamentous; margin filiform; Taxonomic notes: The LSU region of the ex-type strain of A. gracilis var. sartoryi (NRRL 4745; GenBank U29642) is identical with the ex-type strain of A.
Chemical composition and thermal analyses The following analyses were carried out in duplicates according to known analytical methods: moisture and volatile solids by French standard NF V 03-903  [18] , minerals by French standard NF V 03-322  [19, 20] , cellulose, hemicelluloses and lignins by ADF-NDF method from Van Soest and Wine  [21, 22]. Water-soluble components were estimated through measurements of mass reduction of the test sample after 1 h in boiling water. Thermogravimetric analysis (TGA) of PHTr fraction was performed with a Shimadzu TGA-50 (Japan) analyzer. The sample was first equilibrated in a climatic chamber (60% RH, 25 °C) for three weeks, and the test sample mass was about 7 mg. Sample weight was measured as a function of temperature, and the data used subsequently to plot the percentage of the residual sample mass (W), relative to the starting sample weight (W 0 ), as a function of temperature.
Acknowledgements This work was carried out also within the framework of the  COST-European Cooperation in Science and Technology EUBis Action   TD1203 , specifically through the  COST-STSM- TD1203-26977. The authors would like to thank  William Tapia  ( Laboratoire de Chimie Agro-industrielle, Université de Toulouse, INRA, INPT, France ) for the precious help in the EVOH grinding procedure. Thermopressing experiments and mechanical characterizations of fiberboards were conducted in the AGROMAT platform (Tarbes, France), the technological transfer hall of LCA laboratory dedicated to the production of agromaterials.
RNA-sequencing data computational analysis The initial whole transcriptome paired-end reads obtained from sequencing were mapped against the latest version of the human genome (version GRchr37/hg19) by using the Life Technologies mapping algorithm (http://www. lifetechnologies. com/; version 1. 3). The aligned records were reported in BAM/SAM format  [15]. The Picard Tool (http://picard. sourceforge. net/; version 1. 83) was used to eliminate insufficient quality reads (Phred score < 10). Subsequently, gene predictions were estimated using the Cufflinks method  [16] , and the expression levels were calculated by using HTSeq software  [17]. This method eliminates the multimapped reads, so that only the unique reads are considered for gene expression estimation. The differential expression analysis between conditions was assessed by the edgeR method (version 3.
2. 4) [18]. This method relies on different normalization processes based on the depth of global samples, the CG composition, and the gene length. Moreover, this method is based on a Poisson model that estimates the variance of the RNA-sequencing data for differential expression. Finally, we selected genes showing differential expression at a significance threshold of P < 0. 05. The data presented in this paper have been deposited in NCBI's Gene Expression Omnibus (GEO)  [19]  and are accessible through GEO Series accession number GSE55296 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE55296).
Clinical characteristics of DCM patients We analyzed 23 LV tissue samples from 13 patients with DCM undergoing heart transplantation, and 10 hearts from non-diseased CNT donors for RNA-sequencing analysis, increasing the sample size up to 20 and 25 DCM samples for RT-qPCR and for western blot experiments, respectively. A majority of the patients were men (92%, 70% and 76%, respectively), and were previously classified with an NYHA functional classification of III-IV and diagnosed with significant comorbidities, including hypertension and diabetes mellitus. Table  1  summarizes the clinical characteristics of the patients included in the study. The CNT group was also mainly composed of men (80%), with a similar mean age of 47 ± 16 years. Comorbidities and other echocardiographic data were not available for the CNT group, in accordance with the Spanish Organic Law on Data Protection 15/1999.
EEG data acquisition and analysis Dense-array EEG data (HydroCel Geodesic Sensor Net, EGI System 300; Electrical Geodesic Inc. , OR, USA) was collected from 256 channels (band-pass filtered at 0. 01-100 Hz with a vertex electrode as a reference) at a sampling rate of 250 Hz and recorded with Net Station Software (Version 4. 5. 1, Electrical Geodesic Inc. , OR, USA). Further offline data analysis was conducted with the open-source EEGLAB toolbox  (Delorme and Makeig 2004). Data were digitally filtered to remove frequencies below 0. 5 Hz and above 35 Hz. Average reference was recomputed and bad channels were automatically removed by kurtosis measures with a threshold value of 20 standard deviations. The choice of the threshold was adjusted based on our previous study  (Beldzik et al. 2015a)  to ensure that only true outliers (usually 5% of channels) were automatically removed; for instance, electrodes not adhering to the scalp.
Next, continuous data were visually inspected to remove channels or time epochs containing high amplitude, high-frequency muscle noise and other irregular artefacts. The removed channels were interpolated. Independent component analysis at the individual level was used to remove artefacts from the data. Due to the large number of channels, decomposition of EEG data with the Infomax algorithm was preceded by Principle Component Analysis. Fifty ICs were obtained and visually inspected for each subject. According to the characteristic spatiotemporal pattern  (Jung et al. 2000; Delorme and Makeig 2004) , components recognized as blinks, heart rate, saccades, muscle artefacts, or bad channels were removed. Next, epochs were extracted from -400 to 1200 ms with regard to stimulus presentation and were baseline corrected from -100 to 0 ms.
e. 191). The number of independent sources to extract was set to seven based on our previous study with the numerical task  (Beldzik et al. 2015a). The stability of ICA decomposition was validated with ICASSO  (Himberg et al. 2004) , with 50 random initializations of the Infomax algorithm. Data were back-reconstructed using the moo-icar option, i. e. the multivariate objective optimization ICA with reference  (Du and Fan, 2013). Since the reconstruction adds some noise to component activity, a 35 Hz low pass filter was reapplied to the time courses with the use of the EEGLab iirfilt() function. To estimate the location in the brain of each independent source, the scalp maps were imported to BESA 5. 3 (MEGIS Software GmbH, Gra ¨felfing, Germany) and underwent Classical LORETA Analysis Recursively Applied (CLARA).
fMRI data acquisition and analysis Magnetic resonance imaging (MRI) was performed using a 3 T scanner (Magnetom Skyra, Siemens) with a 20-channel head/neck coil. High-resolution, whole-brain anatomical images were acquired using a T1-MPRAGE sequence. A total of 176 sagittal slices were obtained (voxel size 1 9 1 9 1. 1 mm 3 ; TR = 2300 ms, TE = 2. 98 ms, flip angle = 9°) for coregistration with the fMRI data. Functional T2*-weighted images were acquired using a wholebrain echo planar pulse sequence (EPI) with the following parameters: 3 mm isotropic voxel, TR = 2000 ms, TE = 30 ms, flip angle = 90°, FOV 192 9 192 mm 2 , GRAPPA acceleration factor 2, phase encoding A/P. The whole-brain image (cerebellum excluded) was covered with 35 axial slices, taken in an interleaved, ascending fashion. The acquisition time for each session was 15 min.
Due to magnetic saturation effects, the first four volumes (dummy scans) of each session were discarded instantly. The standard pre-processing procedure was applied using Analysis of Functional NeuroImage (AFNI) software  (Cox 1996). Each 3D image was time-shifted so that the slices were aligned temporally. After head motion correction, the functional EPI data sets were coregistered to structural scans and smoothed using a full-width at a half maximum isotropic Gaussian kernel of 4 mm. Voxels with low-signal intensity located outside the brain were excluded from the functional images by a clipping function. During the scaling procedure, the percent signal change was calculated for each voxel. Anatomical and functional images were transformed into a coordinate system of Talairach space  (Talairach and Tournoux 1988).
Next, group ICA was conducted using the GIFT toolbox  (Calhoun et al. 2001). The estimation determining the number of components was performed using the minimum description length (MDL) criteria implemented in the software  (Li et al. 2007 ). The stability of ICA decomposition was validated with ICASSO  (Himberg et al. 2004) , with 50 random initializations of the Infomax algorithm. In the case of insufficient stability scores, ICA was performed with the numbers neighbouring the number estimated with the MDL criteria. Data were back-reconstructed using the gica template option. The group average maps were inspected to identify and discard those ICs primarily associated with artefacts that represented signals from large vessels, ventricles, motion, and susceptibility. From the remaining ICs, time-courses were interpolated to 100 ms resolution and epochs were extracted -1 to 12 s prior to the stimulus.
EEG results Seven independent sources were reliably obtained (component stability was above 0. 98) with the group ICA. Each of the estimated EEG sources can be characterized by its topography (Fig. 3a ), the constellation of underlying brain sources (Fig. 3b ), and event-related time courses (Fig. 3c ). Local maxima of independent component source reconstructions are listed in Table  4. Three of the components had clearly identifiable ERPs and matched the independent components obtained previously  (Beldzik et al. 2015a). The fronto-central component with the source estimated in the medial frontal cortex showed early (* 100 ms after the stimulus onset) negativity, which can be linked to the visual anterior N1 potential  (Vogel and Luck 2000). The activity of this component in response-locked data revealed pronounced ERN and followed it positivity  (Holroyd and Coles 2002).
Material and Methods Bee records in the EIS (European Invertebrate Survey Netherlands) database in the period 1946-2013 are analysed, extending periods investigated in  Biesmeijer et al. (2006)  and  Carvalheiro et al. (2013). Sampling has not occurred in a standardized manner across the study period (Figure  1 ). In particular from mid-eighties until around 2005, increasingly larger numbers of records were collected, more often by observation and with records from a much larger number of 10x10 km grid cells. The samples show important time trends in the heterogeneity parameter  of Poisson lognormal distributions when fitted to the numbers of records per species in each year  (Engen et al. 2002). Here, this parameter  is a compound measure representing abundance variation between species rescaled by species-specific sampling effort if present.
Temporal patterns in estimated species richnesses are expected to reflect these changes in sampling strategy on top of the actual biodiversity changes. I assessed patterns of species richness change over time in the entire Netherlands. To facilitate comparisons with  Carvalheiro et al (2013) , time patterns in species richness of Bombus and non-Bombus bee genera in the EIS database were analysed separately and in different ways, which each tried to take changes in sampling effort and method over time into account. Each of these approaches should be seen as an attempt to arrive at a satisfactory model for richness change, where the inference is kept conservative with respect to claims of richness recovery, i. e. , a rejection of the null hypothesis of no deceleration in richness decline should not be spurious.
Then models with one d. f. added to each spline in this model or with one d. f. removed were also fitted, to check whether these modifications would reduce the AICc further. The model with the lowest AICc among all the ones fitted is called the minimum "adequate" model. This model is compared with the "maximal" model: the model with lowest AICc among those with splines of 8 d. f. for both a(t) and b(t). To assess estimation bias, a corrected analysis was carried out, using a bootstrap estimate of bias  (Davison & Hinkley 1997). One hundred bootstrap pseudo-datasets were constructed by randomly drawing for each year a number of individuals equal to the number of records in that year and repeating the analysis above for each of these sets of pseudo-data. The covariates calculated from the original data were kept except for the multinomial sampling variances which were re-calculated for each sample.
The maximal and adequate models were fitted to each of these datasets, and species richnesses per year predicted. The estimated bootstrap bias equalled the average of these predictions per year minus the predicted value obtained from the original data. Note that this straightforward bootstrap differs from the one proposed by  Chao et al. 2014 , in that the number of undetected species is not estimated and incorporated in the resampling and that it is not used to obtain an unconditional estimate of sampling variance. Capture-recapture analysis of species encounter histories Suggestions that methods of the capture-recapture framework should be applicable to assemblages of species encounter histories have been made repeatedly  (Nichols & Pollock 1983; Nichols et al. 1998; MacKenzie et al. 2006).
In this framework, every species encounter history is a sequence of zeroes and ones indicating for which years at least one record is present in the dataset. However, the dataset and the subsets analysed here have no repeated within-year occasions where the assemblage can be assumed to be closed, such that a robust design model cannot be fitted  (Pollock 1982)  and temporary emigration cannot be estimated in that manner. Temporary emigration can also be estimated assuming an unobservable state  (Kendall & Nichols 2002 , Schaub et al. 2004 ). Such a model as constructed for capture histories of individuals cannot deal with individual species jointly present inside and outside of the Netherlands and we would have to use occupancy models. For parameter estimability, we would further have to assume the absence of time trends in emigration and survival  (Schaub et al.
2004 ) such that the time-dependent models of interest cannot be fitted. Therefore, encounter histories per species were analysed using capturemark-recapture analysis for open populations  (Pradel 1996)  and trends in temporary emigration were assessed otherwise. Models were fitted to species encounter histories across years using Mark software  (White & Burnham 1999 ) called via RMark in R (Laake 2013). I parameterized using the "Pradrec" model, which estimates local survival and colonization, where I note that the first is a probability per species (in between zero and one) and the second the number of colonizing species relative to the number present at the beginning of a time interval (non-negative). Local survival probabilities, species colonization and detection probabilities were estimated as being constant, with a linear trend over time (in the linear predictor), with categorical effects per year ("time-dependent") or with regression models that have the total number of records, number of grid cells with records per year, and  of the Poisson lognormal distribution of the taxon concerned as year-specific covariates ("regression").