dataesr/scientific-paragraphs-categorization huggingface dataset:
The first analysis (I) directly estimated the effects of protection on bird diversity while the two others (II and III) investigated the underlying mechanisms to explain the results of the first analysis. Analysis I quantifies the effect of protected areas on bird diversity through models contrasting bird diversity of checklist sites between protected versus unprotected sites, while controlling for protected area location biases (and other potential confounding factors) I: Bird Diversity protection þ location biases þ control: ð2Þ Analysis II quantifies the effectiveness of protected areas at mitigating forest loss and forest degradation, through models controlling for location biases and spatial autocorrelation. To measure the effects of protection on forest loss (IIa), we built logistic models contrasting protected versus unprotected background sites in their probability of being forested with land cover data IIa: Forest presence protection þ location biases þ te lon; lat ð Þ : ð3Þ We have also run an analysis IIa′ comparing forest loss rates (log transformed to fit normal distribution) between protected and unprotected sites IIa 0 : log 0:001 þ Forest loss ð Þ protection þ location biases þ te lon; lat ð Þ : ð4Þ To measure the effects of protected areas on forest degradation (IIb), we built Gaussian models contrasting protected versus unprotected background forested sites in terms of forest quality (canopy height, forest contiguity, or wilderness) IIb : Forest quality protection þ location biases þ te lon; lat ð Þ : ð5Þ Analysis III quantifies the effects of forest presence (IIIa) or of forest quality (IIIb) on bird diversity, while controlling for potential confounding factors.
Acknowledgements We are grateful to the numerous observers who contributed records to eBird, and to the eBird team at  Cornell Lab of Ornithology  for creating and managing this database. J. K. was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  766417. This communication reflects only the authors' view and the  Research Executive Agency of the European Union  is not responsible for any use that may be made of the information it contains. K. P. was funded by the  French National Research Agency  under the grant agreement No  ANR-17-CE04-0012.
Introduction: Ribosomal DNA (rDNA) loci have been widely used for identification of allopolyploids and hybrids, although few of these studies employed high-throughput sequencing data. Here we use graph clustering implemented in the RepeatExplorer (RE) pipeline to analyze homoeologous 5S rDNA arrays at the genomic level searching for hybridogenic origin of species. Data were obtained from more than 80 plant species, including several well-defined allopolyploids and homoploid hybrids of different evolutionary ages and from widely dispersed taxonomic groups. Results: (i) Diploids show simple circular-shaped graphs of their 5S rDNA clusters. In contrast, most allopolyploids and other interspecific hybrids exhibit more complex graphs composed of two or more interconnected loops representing intergenic spacers (IGS).
200-1,000 bp)  (Sastri et al. , 1992; Fulnecek et al. , 2006)  falls within the range defined for satellite DNA, allowing circularization of chains of overlapping reads. In this study we investigated the 5S rDNA genomic organization and homogeneity in more than 80 plant diploids and polyploids, exploiting high-throughput reads available from read archives in public genomic databases and also de novo sequenced by us. Particular attention was paid on hybrid systems with well-defined evolutionary histories, both eudicots and monocots: (i) Brachypodium hybridum (Poaceae), Brassica carinata (Ethiopian mustard, Brassicaceae), Chenopodium quinoa (quinoa, Amaranthaceae), Gossypium hirsutum (cotton, Malvaceae), and Nicotiana rustica (Aztec tobacco, Solanaceae) allotetraploids. (ii) Spartina × townsendii (cordgrass, Poaceae) homoploid hybrid.
The total k-mer coverage scores (mean of cluster homogeneity) were high in diploid species while they were lower in the allotetraploids (Table  1 ). The connected component index C (mean of graph circularity) was uniformly high across the species. The read richness varied between the loops. For example, in Brachypodium hybridum, the right loop contained far more reads than the left loop. In the whole dataset (Supplementary Table  S1 ) typical circular graph shapes of 5S rDNA clusters (type 1) were obtained in 81 (96%) species. The connected component index C parameter values (reported by a TAREAN) were high, ranging from 0. 684 to 1. 00 (average 0. 959, s. d. 0. 0599). In three species (4%), no circularization of 5S graphs clusters was obtained. Figure  2A  shows the frequency of individual cluster types in allopolyploid and diploid species.
, 2017). Cluster graph topologies were visually inspected and categorized into two groups (simple, type 1, and complex, type 2, graphs). The k-mer score was calculated by the RepeatExplorer2/TAREAN program as the sum of frequencies of all k-mers used for consensus sequence reconstruction. Identification and Quantification of Homoeologous 5S rRNA Gene Families 5S rDNA homoeologous families were quantified by mapping analysis using CLC Genomics Workbench (QIAGEN), CLC onwards. Trimmed reads (typically >7 million) were mapped to the corresponding reference with following parameters: insertion and deletion costs = 3, lengths fraction = 0. 5, similarity fraction = 0. 9, deletion cost = 2. As reference sequences we used Gossypium arboreum (GenBank no. GAU31855) and G. raimondii (GRU39497) clones.
Since no FIGURE 6 | Graph structures of 5S rDNA clusters from the RepeatExplorer2 graphical output containing a significant number of retroelement sequences in Tragopogon porrifolius (A), Senecio vulgaris (B) and Musa acuminata (C). In (A, B) reads derived from a Cassandra/TRIM element. Note, the Cassandra/TRIM elements were relatively isolated and connected to 5S rDNA with 1-2 reads only. In (C) the highly complex structure of 5S rDNA in M. acuminata containing multiple IGS and regions of similarity to a reverse transcriptase domain of a TY-1 Copia retroelement. GenBank 5S rDNA clones were available for Brachypodium we used consensus sequences of B. distachyon (370 bp) and B. stacei (270 bp) generated by RepeatExplorer2 as a reference. Phylogenetic analysis was carried out using assembled contigs computed by RepeatExplorer2.
Briefly, BLAST libraries of contigs from hybrids and allopolyploids were BLASTed against the 5S rDNA sequences: For Gossypium, these were GenBank clones (#GAU31855 and #GRU39497); for Brachypodium and Spartina contigs generated by RepeatExplorer were used. The IGS subregions were extracted from BLAST outputs by a selection command in CLC, grouped and aligned. Alignments were manually edited and neighbor joining phylogeny trees constructed (CLC). Because homoeologous ITS1 (internally transcribed spacer 1 of 35S rDNA) cannot be quantified by mapping procedures due to their overall similarity, we calculated the ITS1 homoeologous ratios from the number of nodes in genome-specific clades of phylogeny trees: (i) ITS sequences from Gossypium and Brachypodium allotetraploids were extracted from mapped reads, yielding typically hundreds to thousands of sequences.
(ii) Stand-alone BLAST databases were generated from the ITS sequences. The databases were queried with reference sequences derived from variable 50-70 bp central subregions of ITS1. The ITS1 consensus sequences were obtained from the alignment of GenBank clones: B. stacei (JX665827-JX665832), B. hybridum (JX665718-JX665731), G. arboreum (GAU12712), and G. raimondii (GTU12711). (iii) Reads extracted from BLAST outputs were trimmed to uniform length, sampled (100-500 reads), and NJ trees constructed using a phylogeny tool of CLC. Homoeologous sequences in distinct clades were extracted, counted, and expressed as a ratio.
Southern Blot Hybridization Total genomic DNA was extracted from fresh young leaves using a modified CTAB method following procedures described previously  (Kovarik et al. , 1997). Genomic DNA was digested with the BamHI restriction enzyme and hybridized on blots. The DNAs were digested with BamHI and hybridized with the radioactively labeled ([32P] dCTP, Dekaprimer labeling kit (Thermo Fischer, USA) 5S rDNA probe. The probe was a trimer of the 5S rRNA gene from Artemisia tridentata [S4 clone, GenBank # JX101915. 1, (Garcia et al. , 2012b) ]. Hybridization signals were visualized using a PhosphorImager (Fuji, FLA 9000).
ACKNOWLEDGMENTS We thank members of the  Malika Ainouche ,  Andrew R. Leitch  and  Martin Lysak  laboratories for their kind help with Spartina and Cardamine sampling. We further thank the editor and reviewers for their helpful comments and the organizers of annual RepeatExplorer meetings in  Ceske Budejovice  ( Czech Republic ). Access to computing and storage facilities owned by parties and projects contributing to the  National Grid Infrastructure MetaCentrum  provided under the program Projects of Large Research, Development, and  Innovations Infrastructures  ( CESNET LM2015042 ) is greatly appreciated.
MATERIALS AND METHODS Molecular Dynamics with excited Normal Modes (MDeNM)  (Costa et al. , 2015)  simulations were carried out on full-length (residues 1-185) farnesylated and methylated (FME) K-Ras4B in its inactive GDP-bound and active GTP-bound forms. The starting coordinates of the catalytic domain were taken from crystal structures with PDB ID 4OBE  (Hunter et al. , 2014)  and 3GFT, respectively. For the active state, the GTP analog GppNHp was modified to GTP, and His61 was mutated back to the native Gln61. MDeNM simulations and analysis were performed with CHARMM  (Brooks et al. , 2009)  using CHARMM all-atom additive force field C36  (Best et al. , 2012)  while conventional MD simulations were carried out with NAMD  (Phillips et al. , 2005)  using the same CHARMM force field mentioned above.
The GDP/GTP parameters were combined from the ADP and guanine parameters existing in CHARMM, while the parameters for farnesylated Cys were taken from our previous studies  (Jang et al. , 2016b). Our main objective in carrying out the MDeNM simulations is a deep exploration of the conformational preferences of the two Ras forms. The structures of the catalytic domain deposited in Protein Data Bank differ in length (e. g. , in 4EPT the structure is determined up to amino acid 166; in 3GFT up to 167, in 4OBE to 169), indicating that the C-terminal amino acids have higher fluctuation. Consequently, for the GDP bound state we created three initial structures with different HVR orientations. In these structures, the HVR was built starting at residues 167, 168, and 169, respectively, from the internal coordinate table of CHARMM, yielding a linear conformation distant from the catalytic domain.
MDeNM Simulations In order to map the conformational space more efficiently than with classical MD simulations, the Molecular Dynamics with excited Normal Modes (MDeNM)  (Costa et al. , 2015)  method was used. The normal modes necessary for the MDeNM simulations were calculated by considering the final structures resulting from the 5 ns MD run for both GDP-and GTP-bound FME full-length K-Ras4B. The energy of the two structures was minimized using the steepest descent method, the harmonic force constant decreasing every 1,000 steps, adopting the values 10, 1, 0. 1, and 0 kcal/mol/Å 2 , followed by 50,000 steps of adopted basis Newton-Raphson method. After the energy minimization, the normal modes were calculated using the VIBRAN module of CHARMM. For the MDeNM calculations, based on their RMSF contribution, the 10 lowest frequency normal modes were taken.
TBFMM, for task-based FMM, is a high-performance package that implements the parallel fast multipole method (FMM) in modern C++17. It implements parallel strategies for multicore architectures, i. e. to run on a single computing node. TBFMM was designed to be easily customized thanks to C++ templates and fine control of the C++ classes' inter-dependencies. Users can implement new FMM kernels, new types of interacting elements or even new parallelization strategies. As such, it can be used as a simulation toolbox for scientists in physics or applied mathematics. It enables users to perform simulations while delegating the data structure, the algorithm and the parallelization to the library. Besides, TBFMM can also provide an interesting use case for the HPC research community regarding parallelization, optimization and scheduling of applications handling irregular data structures.
Statement of need The FMM is a major algorithm but it remains rare to have it included in HPC benchmarks when studying runtime systems, schedulers or optimizers. The principal reason is that it is tedious to implement and requires a significant programming effort when using the task-based method together with the group-tree. However, it is an interesting, if not unique, algorithm to study irregular/hierarchical scientific method. For the same reason, it is difficult for researchers in physics or applied mathematics to implement a complete FMM library and to optimize it for modern hardware, especially if their aim is to focus on approximation kernels. Therefore, TBFMM can be useful for both communities. Among the few existing FMM libraries, ScalFMM is the closer package to TBFMM. ScalF MM supports lots of different parallel strategies, including fork-join implementations, and it contains several experimental methods.
Consequently, ScalFMM has around 170K lines of code, for only 50K for TBFMM. Moreover, it needs several external dependencies and does not benefit from the new standard C++ features that could improve code readability. Besides, it only works for 3D problems, whereas TBFMM's tree and algorithms supports any number of dimensions. These have been the main motivations to re-implement a lightweight FMM library from scratch that only supports task-based parallelization. However, the interface of the kernels is very similar in both libraries, such that creating a kernel for ScalFMM or TBFMM and porting it to the other library is straightforward. FMMTL  (Cecka & Layton, 2015)  is another existing FMM library with a generic C++ design. Similar to TBFMM, FMMTL works with various types of kernels. However, it relies on a fork-join parallelization strategy on top of OpenMP and on the Thrust library to support GPUs.
Kernel As stated in the objectives, TBFMM is a tool for scientists from physics and applied mathematics to create new kernels. TBFMM offers a convenient way to customize the kernel and to benefit from the underlying parallelization engine automatically. With this aim, a user has to create a new kernel that respects an interface, as described by the package documentation. The current package contains two FMM kernels, the rotation kernel based on the rotation-based operators and the spherical harmonics  (Dachsel, 2006; Haigh, 2011; White & Head-Gordon, 1994 , 1996) , and the uniform kernel based on Lagrange interpolation  (Blanchard et al. , 2016; Blanchard, Coulaud, Darve, & Bramas, 2015; Blanchard, Coulaud, & Darve, 2015).
Parallelization TBFMM has two task-based parallel algorithms based on two runtime systems: OpenMP version 4 (OpenMP Architecture Review Board, 2013) and SPETABARU (Bérenger  Bramas, 2019). Both are optional, such that the library can be compiled even if the compiler does not support OpenMP or if the Git sub-module for SPETABARU has not been activated. OpenMP is an API that evolves slowly, which maintains backward compatibility and which is implemented by different libraries that respect the standard. On the other hand, SPETABARU is our task-based runtime system that we use for research, and which continuously evolves. The data accesses of the FMM operators in write are usually commutative (E. Agullo et al. , 2017). While SPETABARU supports commutative write access, OpenMP only supports it from version 5 with the mutexinout data access.
Vectorization (Inastemp) When implementing a kernel, some parts can rely on well-optimized numerical libraries, such as BLAS or FFTW, however, others might be implemented directly in C/C++. In this case, it usually provides a significant improvement in performance to vectorize the code, which allows benefiting from the SIMD capability of modern CPUs. With this aim, TBFMM can include a vectorization library called Inastemp (Berenger Bramas, 2017) by simply cloning the corresponding Git sub-module. Using Inastemp, it is possible to write a single code with an abstract vector data type and to select at compile time the desired instruction set depending on the CPU (SSE, AVX, SVE, etc. ). In the current version of TBFMM, the P2P operator of the two kernels that are provided for demonstration is vectorized with Inastemp.
Hippocampal PAPs contain protein synthesis and maturation organelles To test for the existence of local translation in PAPs from the hippocampus, we first characterized the mRNA distribution in astrocytes. We performed a FISH experiment on adult mouse CA1 hippocampal sections in order to detect Slc1a2 mRNA, a transcript coding for the astrocyte-specific glutamate transporter GLT1. The samples were co-immunostained for the astrocyte-specific intermediate filament glial fibrillary acidic protein (GFAP)  (31). As shown in Fig. 1A , Slc1a2 mRNA FISH dots appeared to be distributed throughout the astrocyte. We used our recently developed AstroDot ImageJ plugin to characterize the mRNA distribution by counting the number of FISH dots localized on GFAPimmunolabeled intermediate filament in the somata and the large and fine processes  (31).
Gene ontology (GO) analyses We used the text-mining Pathway Studio ResNet database (Ariadne Genomics, Rockville, MD) and the gene set enrichment analysis (GSEA) tool  (87)  in Pathway Studio (version 12. 1. 0. 9) (88) to identify signaling pathways and biological processes that were overrepresented in our list of differentially expressed genes. To determine pathways for PAPs and whole astrocytes, we used the GO tool (categories: Biological Processes and Cellular Components; p-value threshold: 0. 01; percentage overlap: >15%). To compare the lists from PAPs vs. whole astrocytes, we selected the Kolmogorov-Smirnov test in the GSEA tool (p-value threshold: 0. 05).
The data were analyzed (read filtering, mapping, alignment filtering, read quantification, normalization and differential analysis) using the Eoulsan pipeline  (90). Before mapping, adapters were removed (using TrimGalore (91) version 0. 4. 1), poly N read tails were trimmed, reads ≤ 40 bases were removed, and reads with a mean quality score ≤ 30 were discarded. The reads were then aligned against the Mus musculus genome (Ensembl version 84) using STAR (version 2. 5. 2b) (92). Alignments from reads matching more than once on the reference genome were removed using a Java version of samtools  (93). The annotated Mus musculus gene transfer format file (Ensembl version 84) was used to compute gene expression. The regions in which alignments and referenced exons overlapped were counted using HTSeq-count (version 0.
5. 3) (93). The RNA-seq gene expression data and raw fastq files are available on the GEO repository (www. ncbi. nlm. nih. gov/geo/) under the accession number GSE143531. The sample counts were normalized using DESeq2 (version 1. 8. 1)  (94). Statistical processing and differential analyses were also performed using DESeq2 (version 1. 8. 1). The presence of an mRNA in whole astrocytes or in PAPs was defined by a read count ≥ 500 and ≥ 70% exon coverage. Enrichment in PAPs was defined as a log2 fold-change ≤ -1 and a p-value ≤ 0. 05, whereas depletion was defined as a log2 fold-change ≥1 and a pvalue ≤ 0. 05.
Confocal images were then deconvolved using the Huygens software and combined with the STED images in one file for analysis. For each astrocyte, 4 regions of interest of about 600 µm² were imaged. Homer1 and VGluT1 stainings were acquired in both confocal and STED resolution. The analysis was performed using an in-house developed plugin on ImageJ to analyze the distance from an identified synapse to the closest astrocyte process. In brief, maxima intensities were identified in STED images and compared to the deconvolved confocal images to avoid false positive punctae. A synapse was defined as two punctae from Homer1 and VGluT1 found within 300 µm of each other. Distances were calculated from the center of the synapse to the closest eGFP-labeled astrocyte process. For puromycylation, transverse slices of 150µm from the hippocampi of a P17 mouse were prepared using a vibrating blade microtome (Leica) in cold (4°C) Artificial cerebrospinal fluid (aCSF) containing (in mM): 119 NaCl, 2.
Fear conditioning Behavioral tests were performed on two-month-old male Aldh1l1:L10a-eGFP, hGFAP-GFP and wildtype mice. On day 1, the fear-conditioning group was placed in a conditioning chamber (characterized by white noise (5000 Hz, 60 dB) and a smell of acetic acid (1%)) and left to explore for 3 min. The mice were exposed to four consecutive 1 s scrambled foot shocks (current: 0. 6 mA; inter-shock interval: ~60 s) and then returned to their home cage. On day 2, the mice were put back in the conditioning chamber to assess contextual conditioning. The time spent freezing during a 5 min session was recorded automatically using a camera Sony effio-e 700TVL and the infrared tracking device. It was quantified using the PolyFear Software. The mice were sacrificed shortly after the freezing test.
Acknowledgments This work was funded by the  Fondation pour la Recherche Médicale  to  M. Cohen-Salmon  ( AJE20171039094 ) and  N. Mazaré  ( FDT201904008077 ), the ED3C doctoral school to  N. Mazaré , the  European Research Council  (Consolidator grant # 683154 ) and  European Union 's  Horizon 2020 research and innovation program  ( Marie Sklodowska-Curie Innovative Training Networks , grant # 722053 ,  EU-GliaPhD)  to  N. Rouach , the  FP7-PEOPLE Marie Curie Intra-European Fellowship for career development  (grant # 622289 ) to  G. Cheung , and the  "Journées de Neurologie de Langue Française"  to  R. Tortuyaux. The creation of the  Center for Interdisciplinary Research in Biology (CIRB)  was funded by the " Fondation Bettencourt Schueller ". We thank  Carole Escartin  and  Océane Guillemaud  for helpful discussions and all members of the  Orion imaging facility  for their high-quality technical support.
The  École Normale Supérieure  genomic core facility was supported by the  France Génomique national infrastructure , funded as part of the  "Investissements d'Avenir" program  managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ). preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted January 23, 2020. ; https://doi. org/10. 1101/2020. 01. 21. 913970 doi: bioRxiv preprint.
The inversion of the system is made with the Symbolic Math Toolbox from Matlab© environment. The simulated time-dependent out-of-plane vibration is then obtained with ( R means real part of the complex valued data): The time-varying vibration maps can be obtained for a chosen sampling frequency f s by defining the discrete time axis as t n = n f s. Simulations are carried out with conditions close to that of experimental measurements in order to get realistic vibration patterns. The simulated aluminum plate has dimensions of L x × L y = 30 × 30 cm 2 , thickness h=2mm, Young's modulus is E = 70GPa , density is ρ = 2. 7 × 10 -3 kg/m 3 , Poisson's coefficient ν = 0. 33 and the loss factor is η E = 1 × 10 -3. The excitation is localized at [x 0 , y 0 ] = [20, 20]cm. The useful rectangular area cut out is limited to x ∈ cm and y ∈  [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] cm.