dataesr/scientific-paragraphs-categorization huggingface dataset:
We also included the LTSER platforms that were listed for 2010 as accredited European platforms  (Mirtl et al. 2013, p. 434). Because only 43 out of the 67 LTSER platforms listed in DEIMS-SDR had designated spatial data in terms of a GIS polygon, to be able to visualize their locations, we created a standardized platform area of 10,000 km 2. This estimate was based on three approaches. First, this is platform size indicated in Fig. 1  Landscape approach according to the architectural metaphor of the LTSER platform concept  (Grove et al. 2013; Mirtl et al. 2013 ) involves (A) siting a landscape as a socioecological system laboratory and engaging stakeholders in knowledge production and learning, (B) constructing by integrating researchers from different disciplines and securing an infrastructure for collecting and analyzing quantitative and qualitative data, and (C) maintaining continuous facilitation and co-ordination to sustain transdisciplinarity(see Table  1 ) the LTSER guidelines (100-10,000 km 2 ;  Mirtl et al.
LTER sites nested within LTSER platforms? To analyze the number of LTER sites contained within the LTSER platforms (see Fig. 2 ) we downloaded the LTER site information from the DEIMS-SDR system. To verify our results, we asked the database manager to provide information on the LTER sites within continental Europe. We were provided with a web link (https://stopopol. github. io/ef_viewer/) that opens an online mapping tool that accesses all accredited LTER Europe sites on DEIMS-SDR, and provides visualization and an export tool. The ''LTER Europe sites'' were all sites that belong to the regional group of ''Europe'', and which have been accredited by the respective national network manager. However, we could not align our result from the DEIMS-SDR system and the online tool which extracted the data from same DEIMS-SDR system.
123 both the online mapping tool and non-accredited sites from a direct DEIMS-SDR database download (both downloaded 2017-01-03). The LTER site data was then spatially analyzed using GIS to see which sites were located within the LTSER platforms. We identified the total number of sites, the mean number of sites per platform, how many platforms were without sites, the number of sites located in multiple platforms and platforms containing only formal sites using both the standardized platform areas (n = 67) and the boundaries available in the DEIMS-SDR system (n = 43).
Ukraine has thus practically backwatered in environmental studies for the past 25 years and the process of active brain-drain is ongoing. Bureaucratized and non-transparent procedures including corruption are also issues. The maintenance of the existing research stations and sites in Ukraine are possible mostly due to financial support through EU and other international projects and interested partners (e. g. ,  Kovalova et al. 2010; Medinets 2014; Medinets et al. 2016). Slovakia was in similar situation before entrance to EU in 2004. Although EU funds helped to improve situation impacts of the previous regime can be felt, in an initial phase of democracy which blocks development in comparison to the western part of EU. Thus, the most efficient way to develop a network in such countries with limited financial opportunities would be to implement EU projects that are dedicated to support the establishment of a LTSER platforms network in both EU and former post-Soviet countries.
Regarding the longevity of LTSER platforms, the duration of project funding was predominantly for 1-2 years, and about 30% of the platforms had secured funding for 3-5 years. This was dependent on the success of securing grants. Being a young landscape approach concept, the LTSER platforms in this study covered only the period 2010-2016, at the end of which 27 of the initial 30 LTSER platforms in 2010 remained listed 2016 in the DEIMS-SDR database. Networking activities can be assessed using several types of proxy data. Focusing on the four surveys, the response rates ranged from 40 to 20%, the decrease of which was in parallel to the increasing complexity of surveys. Whether or not LTSER platforms listed in DEIMS-SDR were formally approved at the national level did not affect the response rates.
Acknowledgements We acknowledge funding from  EU Horizon 2020  for the Research Infrastructure Project eLTER, FORMAS (Project Number  2017:1342 ) and the  Lithuanian Science Council  (Project Number  P-MIP-17-107 ). We thank numerous colleagues for inspiring comments to the survey questions and parts of the text, and  Jennifer Holzer  and referees for commenting the emerging manuscript. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
1 Server (http://www. cbs. dtu. dk/services/ChloroP/) and (iii) the Δ75AtDGAT3 truncated form corresponding to the predicted AtDGAT3 coding sequence displayed on the TAIR website until 2017-07-23. Expression plasmid containing AtDGAT3 sequence was a generous gift from Prof. Ivo Feussner. Δ46AtDGAT3 coding sequence was amplified from the above-mentioned plasmid by PCR using Phusion High-Fidelity DNA Polymerase. The forward primer (5′ GGGCATATGTGCAACAA 3′) containing a NdeI restriction site (bold), and the reverse primer (5′ GGGGCTCGAGATATGAGACAGAACCGAGTCC 3′) containing a XhoI restriction site (bold) were designed from At1g48300 sequence. Δ75AtDGAT3 coding sequence was amplified by PCR using Phusion High-Fidelity DNA Polymerase and the A. thaliana cDNA as template (E. coli C00232(E) clone; GenBank Accession number AF344322; Arabidopsis Biological Resource Center).
NanoLC-MS/MS analysis was performed using an Ultimate 3000 LC system (Dionex, Sunnyvale, CA) connected to a LTQ Orbitrap mass spectrometer (Thermo Electron, Waltham, MA) according to  34. Database searches were performed using X!Tandem Pipeline version 3. 3. 3 according to  35. Identifications of peptides were performed using the UniProt and the TAIR databases. Data were filtered according to a peptide E-value smaller than 0. 05 with a minimum of two peptides to identify a protein.
1A. A chloroplast transit peptide located at the N-terminal extremity of the protein is predicted by ChloroP 1. 1 (http://www. cbs. dtu. dk/services/ChloroP/) for A. thaliana, A. hypogaea, and R. communis DGAT3s, but not for T. cacao and V. fordii's proteins. Noteworthy, this predicted transit peptide was absent in the shorter Δ75AtDGAT3 protein version that was used by Hernandez and colleagues to study the subcellular localization of AtDGAT3  12. The conserved residues are scattered in the N-terminal and C-terminal regions of AtDGAT3s. Two polylysine motifs are found in the first half of DGAT3 proteins (residues 84-91 and 141-155 of AtDGAT3). The C-terminal region (residues 261-310 of AtDGAT3 in Fig. 1B ) is the most conserved among DGAT3 proteins. It harbors a domain typical of the thioredoxin-like [2Fe-2S] ferredoxin family (cd02980 from NCBI's conserved domain database,  40  ) with features only found in homologues to this class of ferredoxins.
Purification of recombinant AtDGAT3. AtDGAT3 is predicted to be a soluble protein according to its hydropathy plot (Fig. S1 ). Accordingly, a transient expression of the Δ75AtDGAT3-GFP fusion in tobacco leaves indicated a cytosolic localization  12. Full-length AtDGAT3 coding sequence and truncated versions (Δ75 and. Multiple sequence alignments were edited using the BioEdit program and the BLOSUM62 similarity matrix for shading with a threshold of 75%. Identical residues are highlighted in black and homologous residues are shaded in grey. Conserved motifs are boxed: the two polylysine motifs in blue, a polyserine-rich region in purple and the thioredoxin-like ferredoxin domain in red with its conserved cysteine residues in yellow and highlighted with red asterisks. Putative catalytic motifs identified in A.
Acknowledgements We thank  Marlène Davanture  (PAPPSO proteomic platform) for her expertise in proteomics analysis,  Carine Deruyffelaere ,  Domitille Rérolle  and  Antoine Moniot  for help during cloning and Western blots. The IJPB benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). The  Laboratoire de Chimie des Processus Biologiques  acknowledges financial support from the  French National Research Agency  ( Labex program  DYNAMO,  ANR-11-LABX-0011 ) and the  French EPR CNRS Facility, IR-Renard ,  FRE 3443  for the EPR experiments.
Light microscopy From each of the six animals, 1 micrometer-thick sections, including all the cortical layers from the pial surface to the white matter, were cut from somatosensory cortex using an ultramicrotome (UC6, Leica Microsystems). We selected sections in which we could identify the large cellular arrangements in layer IV, known as barrels, corresponding to the animals' whiskers. Sections were stained with toluidine blue for 10 minutes on a hot plate at 60 degrees and then rinsed twice before imaging with a light microscope (Zeiss, AxioSkop2) at 20 times magnification (Fig  1A ). We acquired 6 images per section, through the entire cortical thickness, then stitched them together using Photoshop software (Adobe). Total cortical thickness was measured as the distance between the pial surface, above layer 1, and the bottom of layer 6, at the point where the white matter starts (Fig  1B ).
Imaging The block used to produce the semi-thin section through the cortical thickness was stuck to an aluminium stub, with conductive carbon paste, and coated with a 10 nm layer of gold (Cressington vacuum evaporation system). Serial images were collected using a focussed ion beam scanning electron microscopy (FIBSEM; NVision 40, Zeiss NTS, Oberkochen, Germany) as described previously  [27]. For the final serial imaging, we used an acceleration voltage of 1. 8 kV with a current of between 340 and 400 pA, and dwell time of 10 μs per pixel. The image stacks were acquired in the middle of layer 1 in each of the animals. The final stacks were aligned using the MultiStackReg addon  [28]  available for the FIJI image processing software (www. fiji. sc). Each one of the volumes from the six mice was between 660 and 1520 μm 3 (four-month-old mice; 849.
Analysis of neuronal morphology From each of the six volumes, a single sub-volume measuring 5 x 5 x 5 μm 3 randomly chosen within each of the image stacks was selected for dense reconstruction of every neuronal element, including all the synapses and mitochondria (Fig  4 ). Axons and dendrites were reconstructed using the carving, semi-automated algorithm  [30]  of the Ilastik software 0. 5 (www. ilastik. org). The synapses were reconstructed using the TrakEM2 software, by drawing the contact site between pre and post-synaptic densities as a single object. The same method was used for mitochondria. To enable rapid and accurate reconstructions on a desktop computer (MacPro, 2 x 2. 4 GHz Quad-Core Intel Xeon, 12 GB 1066 MHz DDR3), image stacks were downsampled to half of their original size prior to importing them into Ilastik  [31, 32].
From the six volumes, 1820 pieces of axon and 478 of dendrite were reconstructed (details in Table  2 ). A total of 1135 synaptic contacts were manually reconstructed. The 3D reconstructions from each volume were exported as object files (. obj) from either FIJI or Ilastik and imported into the Blender software for visualization. Morphological analysis was performed within this program using the NeuroMorph Measurement Tools  [33]  (https:// neuromorph. epfl. ch; Blender version 2. 9, www. blender. org). Each object is represented by a surface mesh comprising vertices, edges, and faces which can be fully annotated using this software. The complete set of images for each of these subvolumes and the corresponding reconstructions are provided here: http://datadryad. org/review?doi=doi:10. 5061/dryad.
bh78sn5 Within each of one of these image stacks, we quantified the volume, surface area, and length of all the axons and their corresponding boutons, as well as dendrites and their spine heads. The surface area of each asymmetric synapse was measured and each of these objects was then assigned an identifier tying it to its presynaptic bouton and postsynaptic spine, or dendrite (details given in Table  2 ). All the data were exported as a single data sheet, and all statistical analyses were then performed using Prism 6. 0 (GraphPad). To measure the 3D arrangement of vesicles in a single bouton we again used NeuroMorph and the Synapse Vesicle Distances tool. Within the center of each vesicle, a sphere was placed which approximately filled the structure. This made it hard to double count vesicles.
Background: Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.
Results: To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Conclusions: Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github. com/variani/lme4qtl.
However, the decrease in computation time comes at the expense of flexibility. In particular, most efficient LMM methods developed for GWAS assume a single random genetic effect in model specification and support simple study designs, for example, prohibiting the analysis of longitudinal panels. We have developed a new lme4qtl R package that unlocks the well-established lme4 framework for QTL mapping analysis. We demonstrate the computational efficiency and versatility of our package through the analysis of real family-based data from the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project  [10]. More specifically, we first performed a standard GWAS, then showed an advanced model of gene-environment interaction  [11] , and finally estimated the influence of data sparsity on the computation time.
R packages for linear mixed models The first group of R packages implement routines to fit linear mixed models as stand-alone programs, for example, the most recent Gaston package  [12]. The second group of R packages were developed as extensions of the lme4 R package, including our lme4qtl package. Of the many existing lme4-based extensions, the closest to lme4qtl is the pedigreemm R package  [13]. Although this package does support analysis of related individuals, the relationships are coded using pedigree annotations rather than custom covariance matrices. Furthermore, the pedigreemm package is not able to fit many advanced models in comparison with lme4qtl (Additional file 1: Supplementary Note 1).
To define the Z * substitution in this case, we use the eigendecomposition of G. Although G is not of full rank, we take advantage of lme4' special representation of covariance matrix in linear mixed model, which is robust to rank deficiency  [14, p. 24-25]. Third, we extend the lme4 interface with an option to specify restrictions on model parameters. Such functionality is necessary in advanced models, for example, for a trait measured in multiple environments (Additional file 1: Supplementary Note 2). We note that the later two features are available only in lme4qtl, but not in other lme4-based extensions such as the pedigreemm package  [13].
Analysis of the GAIT2 data The sample from the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project consisted of 935 individuals from 35 extended families, recruited through a proband with idiopathic thrombophilia  [10]. We conducted a genome-wide screening of activated partial thromboplastin time (APTT), which is a clinical test used to screen for coagulation-factor deficiencies  [18]. The samples were genotyped with a combination of two chips, that resulted in 395,556 single-nucleotide polymorphisms (SNPs) after merging the data. We performed the same quality control pre-processing steps as in the original study: phenotypic values were log-transformed; two fixed effects, age and gender, and two random effects, genetic additive and shared house-hold, were included in the model; individuals with missing phenotype values were removed and all genotypes with a minimum allele frequency below 1% were filtered out, leaving 263,764 genotyped SNPs in 903 individuals available for GWAS.
Results We considered three models for the analysis of APTT in the GAIT2 data, namely polygenic, SNP-based association and gene-environment interaction. Before conducting the analysis, we organized trait, age, gender, individual identifier id, house-hold identifier hhid variables and SNPs as a table dat. The additive genetic relatedness matrix was estimated using the pedigree information and stored in a matrix mat. A polygenic model m1 was fitted to the data by the relmatLmer function as follows. m1 <-relmatLmer(aptt ~age + gender + (1|id) + (1|hhid), dat, relmat = list(id = mat)) The proportion of variance explained by the genetic effect (heritability) was 0. 56, and its 95% confidence interval, estimated by profiling the deviance  [14] , was [0. 45; 0. 84]. We further tested whether the genetic effect was statistically significant by simulations of the restricted likelihood ratio statistic, as implemented in the exactRLRT function of the RLRsim R package  [15].
1 hours, Additional file 1: Table  S1 ). In additional experiments varying the number of fixed and random effects, the lme4qtl package was also several times faster than SOLAR (Additional file 1: Table  S1 , Additional file 1: Figure  S2 ), owing to the efficient lme4 implementation of sparse matrix methods. Though, in a special case when a model has a single random effect, SOLAR had a option to apply the eigendecomposition trick and substantially speed up the computation (3. 8 h), while this option has not been implemented in lme4qtl (6. 6 h). When including a widely used lmekin function from the coxme package  [20]  in the comparison study, our package lme4qtl also showed the lowest computation time (Additional file 1: Figure  S3 ). As comparison with other packages is beyond the scope of this work, we suspect that lme4qtl will likely outperform others or show similar results under scenario of sparse covariance matrices.
We note that the lme4qtl performance substantially declines for dense covariance matrices, as described further below. If one is interested in more complex models than m1 and m2, our package lme4qtl is flexible enough for advanced model specification. For instance, lme4qtl allows for extension of the polygenic model m1 to assess the hypothesis of sex-specificity (a special case of gene-environment interaction)  [11]. The first genetic random effect, denoted as (0 + gender|id), has three parameters σ g 1 , σ g 2 and ρ g and its variance is partitioned among three groups of pairs: male-specific σ 2 g 1 , the genetic variance captured by males , female-specific σ 2 g 2 and male-female pairs ρ g σ g 1 σ g 2. The second random effect, denoted as (0 + dummy(gender)|rid), models the heteroscedasticity in residual variance between the two groups of males and females, where the variable rid is a copy of the individual identifier id variable.
Discussion and conclusions We have extended the lme4 R package, a well-established tool for linear mixed models, for application to QTL mapping. The new lme4qtl R package has adopted the lme4's powerful features and contributes with two key building blocks in QTL mapping analysis, custom covariance matrices and restrictions on model parameters. To our knowledge, the lme4qtl R package is the most comprehensive extension of lme4 to date for QTL mapping analysis. Our package also has limitations. In particular, introducing covariance matrices in random effects implies that some of the statistical procedures implemented in lme4 might not be applicable anymore. For instance, bootstrapping in the update function from lme4 cannot be directly used for lme4qtl models. Furthermore, the residual errors in lme4 models are only allowed to be independent and identically distributed, and ad hoc solutions need to be applied in more general cases, as we showed for the geneenvironment interaction model.
However, this restriction on the form of residual errors may be relaxed in the future lme4 releases, according to its development plan on the official website  [21]. Also, lme4qtl cannot compete with tools optimized for particular GWAS models with a single genetic random effect: lme4qtl allows for association models with multiple random effects. In practice, lme4qtl is mostly applicable to datasets with sparse covariance matrices. Its use in population-based studies with dense matrices may lead to a considerable overhead in computation time. The typical study designs suitable for lme4qtl are family-based studies, longitudinal and similar studies with many sparse grouping factors. Also, lme4qtl would be applicable in a 2-step GWAS procedure even in population-based studies: at the first step, the linear mixed model is fitted a single time under the null hypothesis of no association; at the second step, association tests make use of the variance component parameters estimated at the previous step, thus, avoiding fitting the linear mixed model again and speeding up the computation  [3, 4].
We present the update of the SModelS database with the simplified model results from CMS searches for supersymmetry at Run 2 with 36 fb -1 of data. The constraining power of these new results is compared to that of the 8 TeV results within the context of a full model, the pMSSM. The new database, v1. 1. 2, is publicly available and can readily be employed for physics studies with SModelS.
The principle of SModelS, in the current version 1. 1, is to decompose BSM collider signatures featuring a Z 2 symmetry into simplified model topologies, using a generic procedure where each SMS is defined by the vertex structure and the SM final state particles; BSM particles are described only by their masses, production cross sections and branching ratios. The working principle is illustrated in Fig. 1. The SModelS code and database are publicly available on GitHub at https://github. com/SModelS/ or on the SModelS wiki page, http://smodels. hephy. at/. The previous database version  [8]  (v1. 1. 1) was comprised of 186 results (125 upper limits and 61 efficiency maps) from 21 ATLAS and 23 CMS SUSY searches, covering a total of 37 simplified models. From these 44 searches, the vast majority were based on Run 1 data.