dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the above l t and N tumble maps were used to analyze the simulation results. The present algorithm differs from the calibration process methodology employed by Richard et al. [14]  using 'IFP-Optilab'. In the present study, the initial conditions that  concern the mass fraction of air and of isooctane fuel, intake pressure and temperature are taken directly from experimental data. Residual burned gases, which have a significant impact on the combustion process (heat release, knock, pollutants, etc. ), were estimated by a multi-cycle simulation with five real engine cycles. Only the results of the last cycle were used to calculate the error between incylinder simulation and experimental data and to analyze the results.
ACKNOWLEDGMENTS The authors acknowledge  Stephane Richard  for discussions to improve the AMESim software and the new procedure for residual gases. This study was financially supported by  PSA Peugeot Citroën and Region Centre. This work was also financed by the  MACDOC  project of the  French National Research Agency ,  TDM program ,  ANR-12-VPTT-0008. The authors are also grateful for the support from the  Foundation of Zhejiang Educational Committee  (No. Y201430810 ) and  Natural Science Foundation of Zhejiang Province  (No. LQ15E060001 ), China.
Accuracy Using Synthetic Data MIDAS was subject to a blind test using 50 simulated station coordinate time series (each for east, north, and up) that were previously generated for the Detection of Offsets in GPS Experiment (DOGEx) for purposes of testing step detection methods  [Gazeaux et al. , 2013]. Each of the 150 time series (100 horizontal and 50 up) has a known but undisclosed constant velocity, synthetic steps, gaps, and power law noise. A total of 20 Journal of Geophysical Research: Solid Earth 10. 1002/2015JB012552 automatic step detection programs from different analysis groups around the world were previously tested blindly by  Gazeaux et al. [2013]. Performance can be assessed by comparing the true velocity to the velocity estimated by least squares using all the steps identified by each program (whether true or false).
Acknowledgments This work was supported by  NASA  ACCESS subaward  S14-NNX14AJ52A-S1 ,  NASA   Sea Level Rise  subaward  1551941 ,  NASA ESI  grant  NNX12AK26G ,  NSF   EarthScope grant   EAR-1252210 ,  USGS NEHRP  grant  G15AC00078 , and a  CNES/TOSCA  grant. We are especially grateful to the reviewers  Duncan Agnew ,  Gilad Even-Tzur , and  Simon Williams , who made very helpful suggestions that led to substantial improvements in the paper. We thank  Brian Chung  for conducting preliminary sensitivity tests to seasonality. We thank the  NASA Jet Propulsion Laboratory, Caltech , for providing the GIPSY OASIS II software used to generate GPS time series for this research. We thank  UNAVCO  and  IGS data centers  for providing the GPS data. Time series used for testing MIDAS were obtained through the  Nevada Geodetic Laboratory  web portal http://geodesy.
We observed similar results as in whole cell extracts, except that ORF1p was faintly detected in two additional transformed cell lines (HeLa S3, Hep G2, Figure  1a ). In a complementary approach, we estimated the proportion of L1 transcripts originating from the L1HS-Ta subfamily by counting RNA-seq reads mapped on the L1HS consensus sequence, which encompass subfamily-diagnostic SNPs in the L1 3' UTR sequence (ACA for L1HS-Ta, ACG for L1HS-PreTa and GAG for L1PA2 and older)  (Boissinot et al. , 2000). For this analysis, publicly available data from the hESC line H1 were also included (no stranded polyA+ RNA-seq data were available for MRC-5 and HEK-293 cells). Consistent with L1 RNP quantification, MCF7 and 2102Ep cells exhibit the highest levels of L1HS-Ta RNA-seq tags. Most other transformed cells have intermediate levels, while HCT 116 and primary fibroblasts (BJ, IMR-90) have extremely reduced L1HS-Ta levels (Figure  1b ).
The expression pattern of individual L1HS-Ta loci in standard growth conditions is highly reproducible, as revealed by the clustering of independent RNA-seq experiments obtained from the same cell line grown in two independent laboratory environments (Figure  4a , MCF7_Cristofari and MCF7_ENCODE samples, Pearson correlation r=0. 830 p<0. 0001), highlighting the non-random nature of this process and the robustness of our overall approach. To expand these observations to a range of cell-types we applied the same RNA-seq-based analysis strategy to the complete panel of cell lines (except MRC-5 and HEK-293 cells for which stranded poly(A)+ RNA-seq data were not available in public databases, Figure  4 -figure supplement 1). The identity of the individual L1HS-Ta loci which are expressed, as well as their levels of expression, varies considerably between cell types (Figure  4a  and Supplementary file 5).
Thus, it appears that shut-off of global L1-regulatory pathways is not a prerequisite for L1 activation in cancer. Instead, we find that only a very limited set of L1 copies, at specific genomic loci, become activated in cancer cells. We note that the presence or absence of polymorphic L1s at these permissive loci, and their degree of transcriptional activation, may represent risk factors for particular cancer types, and more specific biomarkers than global L1 expression or methylation status. Furthermore, we provide here a unique resource consisting of near-complete maps of L1HS-Ta elements present in widely used normal and transformed model cell lines, for which the availability of genomic datasets is regularly increasing (including several tier-1 & tier-2 cell-lines of the ENCODE project).
As expected, the transduced region is flanked by 2 poly(A) tails (poly(T) here since it is located on the reverse genomic strand). DOI: 10. 7554/eLife. 13926. 014 transposable elements in the human genome. Indeed, isolated RNA-seq and ChIP-seq signals resulting from the presence of non-reference L1 copies can only be correctly interpreted if such maps are available. Thus they can act as a platform to fully interpret and profit from the many expanding public datasets generated using the same cell lines. Recently, whole genome sequencing of human tumors has revealed recurrent retrotransposition events stemming from a handful of source elements  (Tubio et al. , 2014) , consistent with the notion that only a fraction of all full-length L1 elements is actually capable of retrotransposition  (Brouha et al.
Cell line authenticity was verified by multiplex STR analysis (PowerPlex 21 PCR system [Promega, Madison, WI], assays performed by Eurofins Genomics [Ebersberg, Germany] as a service provider) and comparison with the DSMZ database (https://www. dsmz. de/services/services-human-and-animal-cell-lines/online-str-analysis. html) or with previously published profiles for H1 and 2102Ep cells  (Josephson et al. , 2007; Mallon et al. , 2014).
Read preprocessing In brief, the reads were trimmed to remove barcodes, low-quality sequence, ATLAS-seq primers and linkers, L1 sequence and poly(A) tail, and to keep only the putative genomic flanking sequences. Sequences shorter than 25 nt after trimming were excluded. More specifically, we first demultiplexed fastq files according to the sample-specific barcode and using the FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/). Then we treated differently reads produced in 5'-and 3'-ATLAS-seq experiments. For 3'-ATLAS reads, we first trimmed barcode sequence at the 5' end, allowing only perfect matches using cutadapt (-e 0 -m 25 options)  (Martin, 2011). Reads without detectable barcode or shorter than 25 bp after trimming were discarded. Second, we trimmed the suppression PCR linker from the 5' end, allowing 5 errors maximum (-e 0.
Reads mapping and clustering Trimmed reads were mapped to the hg19 human reference genome using the Burrows-Wheeler Aligner (BWA) program with the 'mem' algorithm  (Li and Durbin, 2010 ) (-t 4 -M options). Mapped reads were filtered to remove secondary alignment and ambiguously mapped reads (MAPQ20) using SAMtools  (Li et al. , 2009 ) (samtools view with -q 20 -F 260 options). Then, PCR duplicate reads were removed with Picard tools (http://broadinstitute. github. io/picard, MarkDuplicates function), keeping only the longest read as a representative. Reads were considered redundant if they started from the same linker position, which corresponds to the initial genomic DNA break during library preparation. However, it should be underlined that this procedure tends to underestimate the number of independent reads, particularly for clusters with an abundant number of reads.
Detection of 3' transductions in ATLAS-seq sequencing data Demultiplexed 3' ATLAS-seq reads, trimmed for the barcode and ATLAS-linker were successively trimmed at their 3' end using cutadapt to remove the RB3PA1 ATLAS primer, the L1 sequence and a polyT stretch. Parameters were identical to regular ATLAS-seq read processing, except for polyT trimming cutadapt options (-a T{100} which ensures only trimming of the most distal poly(T)). We mapped reads using bwa mem (-M -t 4 options), which allows soft clipping, and samtools view with awk scripts to extracts reads with a CIGAR string indicative of 3' end clipping, and to obtain the genomic location and mapping quality of the clipped part of the read (SA tag). At this stage we only kept read for which both split fragments were able to map unambiguously to the reference genome (MAPQ>15).
At the end of this procedure, L1 copies with reads supporting a particular lineage were annotated as belonging to this lineage. This strategy allowed us to overcome the limitations due to reduced sequence quality within or downstream homopolymers and to identify informative reads spanning putative transductions. The sequences of the lineage-specific tags were as follow: AC002980, GCTTTATTGAGGTGTAACCAGCA; RP, TAAATTTAAAACTTTTTTTTTT; LRE3, CGGAA TAGACATTTTGCTTTTCT. They are identical -or shortened -reverse-complemented version of previously published oligonucleotide sequences used to amplify distinctive L1 lineages (TS-ATLAS)  (Macfarlane et al. , 2013). To verify the sensitivity of this approach, we compared our list of full length insertions with a published dataset of insertions deriving from the same three lineages (AC002980, RP and LRE3)  (Macfarlane et al.
Data Raw ATLAS-seq and RNA-seq data were submitted to the ArrayExpress database (www. ebi. ac. uk/ arrayexpress) under accession number E-MTAB-4676 and E-MTAB-3788, respectively. RNA-seq data contain biological duplicates for untreated cells (two independent RNA extractions), and triplicates for shRNA-treated cells (three parallel lentivirus shRNA transductions, see above). The genomic locations of L1 insertions in the different cell lines are provided in Supplementary file 3. Published RNAseq data used in this work are described in Supplementary file 1, and ChIP-seq data were obtained from the ENCODE Project: H3K9me3 (ENCSR000EWQ), H3K4me3 (ENCSR000DWJ), H3K27Ac (ENCSR000EWR), H3K27me3 (ENCSR000EWP), and Pol2 (ENCSR000DMT). All RNA-seq and ChIPseq datasets contain biological duplicates except HCT 116 RNA-seq (single experiment).
Copy number variation (CNV) data were obtained from the ENCODE Project: BJ (ENCFF628TJX), H1 (ENCFF228MUH), HeLaS3 (ENCFF996ASY), HepG2 (ENCFF074XLG), IMR90 (ENCFF455ARY), K562 (ENCFF486MJU) and MCF7 (ENCFF278UJF). Pre-processing of RNA-seq libraries RNA-seq raw reads were pre-processed to enhance their quality and to avoid a significant loss at the mapping stage. The quality of raw reads was verified for each lane per RNA-seq sample, using the FASTQC (v0. 11. 2) tool (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were trimmed using sliding-window mode from Trimmomatic (v0. 32)  (Bolger et al. , 2014). The minimum quality and read length allowed were 20 and 25, respectively, as they showed the best mapping rates in several tests trying different values of these parameters.
RNA-seq mapping PE trimmed reads were aligned per lane to both the hg19 reference transcriptome and genome obtained from UCSC, using TopHat2 v2. 0. 13  (Kim et al. , 2013). For a better accuracy, the parameter -read-realign-edit-dist was set to 0 (specifying alignment of all reads at each step, returning the best alignment for each read). The -r (insert size) and -mate-std-dev (insert size standard deviation) parameters were estimated using the tool CollectInsertSizeMetrics from Picardtools v1. 128 (http:// broadinstitute. github. io/picard), after mapping a subset of the RNA-seq samples. The other mapping parameters were set to their default values.
Analysis of ChIP-seq datasets This section is related to Figure  3f. Mapped ChIP-seq data were retrieved from ENCODE. The replicates of each experiment were pooled together and read duplicates were removed from the merged bam files. The merged bam files were converted into wig files using 'count' from igvtools, with a window of 10 bp and the median function, and, then, converted into bigWig file with tool wig-ToBigWig from the UCSC tools. ChIP-seq signals were obtained using the 'reference-point' mode of computeMatrix from deeptools, specifying 1 kb upstream and 1 kb downstream of the TSS & TES, which were defined as the extremities of each L1HS copy, and with a window of 50bp and mean function. Using a threshold of 0. 05 on the FPKM L1HS expression values, the L1HS copies were divided in two groups: expressed and non-expressed copies.
Clustering of individual L1-HSTa loci according to their transcriptional activity This section is related to Figure  4a. The transcriptional activity of each L1-HSTa instance was estimated independently for each cell-line in which it is detected by the number of RNA-seq fragments which non-redundantly align to the downstream adjacent 1 kb of genomic sequence, and normalized to the number of non-redundantly aligned reads in the entire RNA-seq dataset (FPKM). For this purpose, mates 1 stranded-specific counting was performed at 1 kb downstream L1HS copies with a dedicated script. Only mates with a MAPQ>= 20 were taken into account. The FPKM for each copy was calculated according to the total number of mapped mates 1 in each library. Clusters of celllines with similar patterns of detectable expression of distinct L1 copies were clustered using the R hclust function (distance measure='euclidean', clustering method='ward').
PCR products were resolved and visualized by 1. 5% agarose gel electrophoresis in 0. 5X TBE buffer and ethidium bromide staining (50 ng/mL, Life Technologies). Raw RNA-seq data retrieved from ENCODE were processed as described above to obtain an FPKM value for each gene. We used the UCSC gene set (hg19 version) obtained from iGenomes (https://support. illumina. com/sequencing/sequencing_software/igenome. html). When an L1 copy was inserted in a region corresponding to several overlapping genes oriented in the same direction, we considered the most expressed gene. Ploidy at each L1 insertion locus and for each cell line analyzed was determined using the copy number variation (CNV) tracks obtained from ENCODE (see 'Data' section). They were deduced from Illumina Human 1M-Duo Infinium HD BeadChip assays and circular binary segmentation (CBS), which segments the genome into amplified, normal, heterozygous deletion, homozygous deletion or uncovered regions.
Acknowledgements We thank the  IRCAN genomics core facility  and  C Baudoin  for Ion Torrent sequencing. We are grateful to the  ENCODE Consortium  and particularly to the laboratories of  T Gingeras  ( Cold Spring Harbor Laboratory, USA ), of  J Stamatoyannopoulos  ( Univ. of Washington, USA), of V. Iyer (Univ. of Texas at Austin, USA), and of P Farnham (Univ. of Southern California, USA)  for generating RNA-seq and ChIP-seq data, to the laboratory of  R Myers  ( Hudson Alpha Institute for Biotechnology, USA ) for generating CNV data, to  PW Andrews (Univ. of Sheffield, UK)  for providing the 2102Ep cell line, to  J-L Garcia-Perez  ( Genyo, Univ. of Granada, Spain ) for providing H1 genomic DNA, to  JV Moran (Univ. of Michigan, USA)  for providing control plasmids for retrotransposition assay, and to  R Tirado-Magallanes  ( ENS, Paris, France ) for help with the ChIP-seq aggregation plots.
This work was supported by grants to GC from the  Fondation ARC pour la recherche sur le cancer (Projet Fondation ARC ,  20141201838 ), the  Canceropole PACA (AAP Emergence) , the  European Research Council  ( ERC-2010-StG 243312 , Retrogenomics), the  French Government (National Research Agency, ANR)  through the  'Investments for the Future ' (LABEX  SIGNALIFE , # ANR-11-LABX-0028-01 ), the  Fondation pour la Recherche Medicale  ( FRM DEP20131128533 ), and  CNRS  ( GDR 3546 ). MK was supported by a PhD fellowship from the  Ligue Nationale Contre le Cancer. DVL. was supported by a  COTACyT-CONACyT international postgraduate fellowship.
With the further development of machine learning algorithms and accumulation of reliable datasets, we hope to obtain a comprehensive landscape of many psychiatric and neurodevelopmental disorders. Guided by this full-spectrum structure, a tailor-made neurofeedback therapy should be optimized for each patient  [7]. The theta rhythm (4-12 Hz) is a prominent network oscillation observed in the mammalian hippocampus and is correlated with spatial navigation and mnemonic processing. Inhibitory interneurons of the hippocampus fire action potentials at specific phases of the theta rhythm, pointing to distinct functional roles of interneurons in shaping this rhythmic activity. One hippocampal interneuron type, the oriens-lacunosum/moleculare (O-LM) cell, provides direct feedback inhibition and regulation of pyramidal cell activity in the CA1 region.
O-LM cells express the hyperpolarization-activated, mixed-cation current (I h ) and, in vitro, demonstrate spontaneous firing at theta that is impaired upon blockade of I h. Work using dynamic clamp has shown that in the presence of frequency-modulated artificial synaptic inputs, O-LM cells exhibit a spiking resonance at theta frequencies that is not dependent on I h  [1]. However, due to the somatic injection limitation of dynamic clamp, the study could not examine the potential contributions of putative dendritic I h or the integration of dendriticallylocated synaptic inputs. To overcome this, we have used a database of previously developed multi-compartment computational models of O-LM cells  [2]. We situated our OLM cell models in an in vivo-like context by injecting Poisson-based synaptic background activities throughout their dendritic arbors.
Our results suggest that, given dendritic synaptic inputs, O-LM cells require somatodendritic I h channel expression to be precisely recruited during the trough of hippocampal theta activity. Our strategy of leveraging model databases that encompass experimental cell type-specificity and variability allowed us to reveal critical biophysical factors that contribute to neuronal function within in vivo-like contexts. Extracellular recordings of electric potential, with a century old history, remain a popular tool for investigations of brain activity on all scales, from single neurons, through populations, to the whole brains, in animals and humans, in vitro and in vivo  [1]. The specific information available in the recording depends on the physical settings of the system (brain + electrode).
Passive and spike properties in response to step current stimulation pulses were computed using the NeuronUnit  [3]  package and compared to their respective, experimentally obtained means of olfactory bulb mitral cell properties found in the NeuroElectro database  [4]. Results reveal that across all models, the resting potential and input resistance property means deviated the most from their experimentally measured means (R input t test p = 0. 02, V rest Wilcoxon-test p = 0. 01). The time constant, spike half-width, spike amplitude, and spike threshold properties, in the order of decreasing average deviation, matched well with experimental data (p > 0. 05) (Fig. 5.
O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits Yasunori Yamada 1   1 IBM Research -Tokyo, Japan Correspondence: Yasunori Yamada -ysnr@jp. ibm. com BMC Neuroscience 2016, 17(Suppl 1):O16 Brain connectivity studies have revealed fundamental properties of normal brain network organization  [1]. In parallel, they have reported structural connectivity abnormalities in brain diseases such as Alzheimer's disease (AD)  [1, 2]. However, how these structural abnormalities affect information processing and cognitive functions involved in brain diseases is still poorly understood. To deepen our understanding of this causal link, I developed two large-scale cortical models with normal and abnormal structural connectivity of diffusion tensor imaging on aging APOE-4 non-carriers and carriers in the USC Multimodal Connectivity Database  [2, 3].
Acknowledgements: The work was supported by the NSF 1422438 grant and by the Houston Bioinformatics Endowment Fund. Correlations among neurons spiking activities play a prominent role in deciphering the neural code. Various models were proposed to understand the pairwise correlations in the population activity. Modeling these correlations sheds light on the functional organization of the nervous system. In this study, we interpret correlations in terms of population coupling, a concept recently proposed to understand the multi-neuron firing patterns of the visual cortex of mouse and monkey  [1]. We generalize the population coupling to its higher order (PC2), characterizing the relationship of pairwise firing with the population activity. We derive the practical dimensionality reduction method for extracting the low dimensional representation parameters, and test our method on different types of neural data, including ganglion cells in the salamander retina onto which a repeated natural movie was projected  [2] , and layer 2/3 as well as layer 5 cortical cells in the medial prefrontal cortex (MPC) of behaving rats  [3].
The result shows that, neuron network built by our algorithm can self-grow and self-organize as the complexity of the input external signals increase. And with the screening mechanism, neuron network that can identify different input external signals is built successfully (Fig. 23 ). Acknowledgements: This work is supported by the Core Electronic Devices, High-End General Purpose Processor, and Fundamental System Software of China under Grant No. 2012ZX01034-001-002, the National Natural Science Foundation of China under Grant No. 61502032, Tsinghua National Laboratory for Information Science and Technology (TNList), and Samsung Tsinghua Joint Laboratory. The efficient organization and communication of brain networks underlies cognitive processing, and disruption in resting state brain network has been implicated in various neuropsychiatric conditions including addiction disorder.
Bridging the gap between model and experiment, our resource allows classifying new channel models and experimental current traces within the topology of all models currently in the database (see Fig. 27B, C ). The ICG framework thus allows for quantitative comparison of ion channel kinetics, experimental and model alike, aimed to facilitate field-wide standardization of experimentally-constrained modeling. Neuronal subthreshold oscillations underlie key mechanisms of information discrimination in single cells while dynamic synapses provide channel-specific input modulation. Previous studies have shown that intrinsic neuronal properties, in particular subthreshold oscillations, constitute a biophysical mechanism for the emergence of nontrivial single-cell input/output preferences (e. g. , preference towards decelerating vs.