dataesr/scientific-paragraphs-categorization huggingface dataset:
Mathematical and computational models of the terrestrial biosphere are essential for understanding potential future changes, and are ultimately the great integrators of our information now. With large and long-term investments such as NASA's ABoVE currently underway, it is critical that a community-wide modeling framework is incorporated into data collection early so that we can add substantially to the value of assimilating currently available data assets. The challenge is in connecting the wide array of datasets and focused science interests to a cohesive and coherent integrated larger picture. A coordinated and supported effort across these field and modeling components will help complete the missing pieces to modeling the complex dynamics and feedbacks of the Arctic-Boreal puzzle.
's NGEE Arctic (Wullschleger et al 2011), ESA's GlobPermafrost (Bartsch et al 2017), the Permafrost Carbon Network (Schuur and Abbott 2011), the International Soil Carbon Network (Jandl et al 2014), the Northern Circumpolar Soil Carbon Database (Hugelius et al 2013), the Study of Environmental Arctic Change (Bromwich et al 2010), the Arctic System Reanalysis (Bromwich et al 2016), the Polar Geospatial Center (Noh and Howat 2015), the National Ecological Observatory Network (Keller et al 2008), the Long-Term Ecological Research network (Hobbie et al 2003), and individual AmeriFlux/FLUXNET sites (Oechel et al 2014). Other agencies such as the Interagency Arctic Research Policy Committee (IARPC) coordinates among some of these networks (Arctic Research and Policy Act 1984);.
Acknowledgments We thank  D Bachelet ,  N French ,  R Grant ,  A Harper ,  K Haynes ,  M Huang ,  A Jain ,  C Koven ,  J Metsaranta ,  M Sikka ,  R Pavlick , and  S Veraverbeke  for survey inputs and/or paper comments. This work was supported by  NASA's Arctic Boreal Vulnerability Experiment (ABoVE) ;  NNN13D504T  (JBF),  NNX17AE44G  (SJG). TAD was supported by the  US Army Engineer Research and Development Center Basic Research  ( 6. 1 ) Program. Computing resources supporting this work were provided by the  NASA   High-End Computing (HEC) Program  through the  NASA Center for Climate Simulation (NCCS)  at  Goddard Space Flight Center. The paper was led from the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration.
Assembly and quality control Illumina reads were checked for adapter sequences and bad-quality read ends using Trimmomatic v0. 36 (Trimmomatic, RRID:SCR 011848)  [33]  with the following parameters: "TruSeq3-PE. fa:2:30:10 LEADING:3 TRAILING:3 SLIDING-WINDOW:4:15 MINLEN:70". Reads with Ns in the sequences were filtered using Sickle (version 1. 33)  [34]. The final cleaned dataset used included reads with an average quality of more than 30, were longer than 70 bp, and were without Ns. The PacBio reads were corrected by the filtered Illumina reads using Proovread (version 2. 14. 0)  [35] , and the corrected reads were further used for the assembly. All sequencing data as well as the genome assembly can be found under accession number PRJEB24056 at the European Nucleotide Archive (ENA)  [36].
Gene prediction Splice alignments of Illumina RNA sequencing (RNA-seq) data (filtered using the same criteria as above for genomic reads, in total 3. 2 Gb) were built using Tophat2 v 2. 0. 10 (TopHat, RRID:SC R 013035)  [43]  using the draft genome. This alignment was used in Blast2GO v4. 1 (Blast2GO, RRID:SCR 005828)  [44]  along with a pretrained dataset from Arabidopsis thaliana. Genes were predicted on both strands. Genes with a length of more than 90 nucleotides with both a start and a stop codon were considered. For the other parameters, default values were opted. Genes were annotated using Blast2GO. For the sequence similarity-based annotation, a locally downloaded protein-RefSeq database  [45]  was queried using the Blastp-fast algorithm of the Basic Local Align-ment Search Tool (BLAST), version 2.
Repeat prediction RepeatScout v1. 0. 5 (RepeatScout, RRID:SCR 014653)  [49]  was used for de novo identification of repeat elements and for generating a repeat element database. This database was used in Re-peatMasker v4. 0. 5 (RepeatMasker, RRID:SCR 012954)  [50]  to predict repeat elements. Putative repeats were further filtered on the basis of their copy numbers. Those repeats represented with at least 10 copies in the genome were retained.
General genomic features For each annotated gene, the shortest distance to the next gene on the same scaffold was measured. In addition, the distance between all heterozygous sites was assessed, as identified by positions with a two-base ambiguity code in the assembly. For this, genomic reads were mapped using MAQ (version 3)  [51] , and positions were scored as heterozygous if the frequency of the lesser base was at least 40%. For the aforementioned analyses, the assembly was divided into nonoverlapping windows of 10-kb size. For each of the resulting 50,994 windows, gene density, GC-content, and genetic diversity were determined. Exon density was measured as the proportion of each window annotated as protein-coding and GC content as proportion of G and C bases. Genetic diversity was approximated by the proportion of heterozygous sites in each window.
Screening for contamination The genic regions of F. sylvatica were blasted against two databases, one containing genes from Arabidopsis thaliana and the other containing genes from Fungi and Straminipila, using an e-value cutoff of 10e -5 and extracting the top hits. The genic regions having a fungus as the top hit were blasted against the non-redundant database from NCBI [52] to reveal whether these were indeed specific to fungi. Local alignments of the genic regions remaining after this filtering process to the supposed fungal homologs were subsequently manually inspected for the distribution of conserved features. In addition, the assembled genome was chopped into 300bp fragments and subjected to analysis with MEGAN (version 5)  [53]. The fragmented genome was blasted against the nucleotide database downloaded from NCBI using an e-value cutoff 10e -8 and a 70% identity cutoff.
Genome summary Raw reads, assembly, and annotations are available from the ENA at accession number PRJEB24056 and at the Beech Genome Resource website  [54]. The genome size was estimated to be 541 Mb based on 15-mer counts (Supplementary Fig. S1 ), while the draft genome assembly was 542 Mb. The assembly comprised 6,491 scaffolds, with 0. 12% Ns. The largest scaffold was 1. 15 Mb, the N50 length was 145 kb, and the L50 count was 983. Also, 58. 36% of the genome is classified as interspersed repeats and around 2% of the genome is comprised of simple repeats. The locations of the interspersed repeats and the simple repeats in the scaffolds are provided as a gff file for download and as a separate track in the genome browser  [54]. In total, 62,012 genes and 73 splice variants were predicted using Blast2Go, of which 58,211 genes had received at least one RNA-seq read support ( 50,723 genes were supported by at least five reads).
Checks for contamination As numerous fungi have been reported to be associated with beech  [29] , special attention was paid to screen for potential fungal contamination. Gene models of F. sylvatica were used as query in a homology-based search using BLAST against two databases, one containing the genes of Arabidopsis thaliana and the other containing genes from Fungi (both extracted from the NCBI nucleotide database), and revealed 222 genic regions with a fungal organism as the top-hit. When these 222 genes were again used as queries in a homology-based search using BLAST against the NR database from NCBI, eight genes were resolved as still having fungal top hits. These eight genes were manually inspected for the distribution of conservation. As conservation was always below a BLAST alignment score of 200 and conserved features were short, there was no conclusive evidence to support that potential contaminant fungi have impacted the assembly.
Two dimensional correlation analysis is explored to data mine the time evolution of the characteristic Raman microspectroscopic signatures of the subcellular responses of the nucleoli of human lung cancer cells to the uptake of doxorubicin. A simulated dataset of experimental control spectra, perturbed with systematically time-dependent spectral changes, constituted by a short term response which represents the initial binding of the drug in the nucleolus, followed by a longer term response of the organelle metabolism, is used to validate the analysis protocol. Applying two dimensional correlation analysis, the in phase, synchronous correlation co-efficients are seen to contain contributions of both response profiles, whereas they can be independently extracted from the out of phase, asynchronous correlation co-efficients.
Raman microspectroscopic data from the previous studies of DOX uptake by and response of the nucleolar regions of A549 and Calu I (Supplemental Material), human lung cancer cells will be employed, and the results will be compared to those of the previous PLSR/ICA analysis. [39] In order to validate the technique, simulated spectral datasets, based on systematic perturbations of the control datasets of the previous experimental studies, will be employed.
Raman Spectroscopy Cells (~ 3 x 10 3 /cm 2 ) were seeded and incubated on (20 mm diameter) CaF2 windows (Crystan Ltd, UK) for 24 hrs for both control and exposure to chemotherapeutic drug. Medium was then removed and samples were rinsed twice with sterile phosphate buffered saline (PBS) and covered with aqueous solutions of DOX, of concentration 0. 6 μM. After each incubation period, cells were washed twice with sterile PBS and fixed in formalin (10 %, 15 min) and allowed to air dry. A Horiba Jobin-Yvon LabRAM HR800 spectrometer with a 785 nm, 300 mW diode laser as source (~100 mW at the sample), Peltier cooled 16-bit CCD, 300 lines/mm grating and 100 μm confocal hole, was used for this work. Spectra were acquired from the three visually identifiable cell locations: cytoplasm, nucleus and nucleolus, in the range from 400 cm -1 to 1800 cm -1 with a x100 objective (LCPlanN, Olympus) for 30 s two times, to finally produce a data set of 30 points per cell location for each cell line, over a total of 210 different cells.
Data Processing and Analysis Raman spectral pre-processing and analysis were performed in Matlab using algorithms developed in house. Prior to analysis, background was subtracted using a NCLS (non-negatively constrained least squares) algorithm, spectra were smoothed (Savitsky-Golay filter 3th order, 11 points), baseline corrected (fifth order polynomial) and vector normalised. Two dimensional correlation analysis was conducted using the MIDAS2010 Graphical User Interface (GUI) originally developed by researchers in the Canadian Light Source for two dimensional correlation analysis and data exploration of time resolved infrared spectra. [42] e GUI is available for download from MathWorks File Exchange,  [42]  and is recommended to be run in Matlab 7. 6 (R2008a), with the Image Processing Toolbox.
2D Correlation Analysis of Simulated Spectral Responses The simulated spectral response data set, including simulated responses over the exposure period of 0-72 hrs, at 2 hour intervals, was analysed using the MIDAS 2010 two dimensional correlation analysis GUI package. [42] Figure  3a  shows the synchronous correlation analysis in contour form, while Figure  3b  shows the same plot, after subtraction of the control spectrum (0 hrs) from the dataset, as a static component. exposed to DOX at the IC50 concentration of 0. 6M (24 hrs, MTT assay) over a period of 0-72 hrs, as reported by Farhane et al. [39]  The zero hour control has been subtracted as a static component. 1445 and 1570 cm -1 , related to skeletal ring vibrations  [43, 44]. In addition, there are a number of weaker peaks evident in the region of ~780 cm -1 , 1000-1100 cm -1 , and ~1600 cm -1 , which represent the cellular responses to DOX exposure  [39].
two dimensional correlation analysis of Raman microspectroscopic signatures of the subcellular interactions of drugs in vitro can identify and differentiate the characteristic signatures of the initial chemical binding of the drug, and the subsequent cellular response, in a single operation. The use of simulated datasets provides a valuable validation of the technique, confirming that the true temporal evolutions of the identified characteristic features are faithfully reproduced. The study further illustrates the potential of Raman microspectroscopic subcellular analysis for cytological processes, and the similarities of the initial binding signatures in different cell lines suggest applications for in vitro preclinical candidate drug screening, which the differences in the response signatures for different cell lines suggest sensitivities of the technique to cellular resistance pathways and potential applications in patient screening for Companion Diagnostics strategies.
Other genes, related to neurotransmitter metabolism, calcium signaling and neuronal structure, were also investigated. Using multivariate mutual information analysis (I k analysis) designed to decipher high-dimensional statistical dependences in datasets, we found that the expression levels of several ion channels were genetically coupled with DA metabolism genes, unravelling a co-regulatory module linking neurotransmitter identity and electrophysiological phenotype. Consistent with previous studies, other genes (including ion channels) displayed significant heterogeneity in their expression pattern. However, the newly identified genetic coupling was found in all midbrain DA neurons, suggesting that the rules underlying the definition and stability of their electrophysiological phenotype are conserved.
Cells were collected by aspiration into borosilicate glass pipettes mounted on a micromanipulator under visual control. Cell dissociation and collection were performed using RNA-protective technique and all solutions were prepared with RNase-free reagents when possible and filtered before use. Electrophysiology recordings, data acquisition and analysis. All recordings were performed as already described previously  6. Picrotoxin and kynurenate were present for all recordings to prevent contamination of the intrinsic activity by spontaneous glutamatergic and GABAergic synaptic activity. Statistical analysis (performed according to data distribution) included: unpaired t test, Mann Whitney, paired t test with a p value < 0. 05 being considered statistically significant. Statistics were performed utilizing SigmaPlot 10.
5 µl 0. 2x assay pool; 0. 5 µl SuperScript III) applying the same thermal profile described above. The pre-amplified products were treated with ExoSAPI (Affimetrix) and diluted 5-fold prior to analysis by qPCR using 96. 96 Dynamic Arrays on a BioMark System (BioMark ™ HD Fluidigm). Data were analyzed using Fluidigm Real-Time PCR Analysis software (Linear Baseline Correction Method and User detector Ct, Threshold Method). Two genes, Kcnj6_c and Chat were undetectable in all analyzed cells. Cells that had a Ct for Hprt above 21 were excluded from further analysis. After interplate calibration, all Ct values were converted into relative expression levels using the equation Log 2 Ex = Ct LOD -Ct (Assay)  66. LOD (limit of detection) was set to Ct = 25 by calculating the theoretical Ct value for 1 single molecule in the Biomark system from two custom-designed oligonucleotides: Slc18a2 and Penk.
All data pre-processing was performed in Microsoft Excel (Microsoft, Redmond, USA). Heatmap and correlation maps (Pearson correlation coefficient values excluding zero values, p value ˂0. 05, n ˃ 5) were generated in the R environment (R Core Team 2016) using gplots, heatmap3, Hmics and corrplot packages. Gene expression scatter plots and frequency distribution plots were created in SigmaPlot 10. 0 (Jandel Scientific) and Prism 6 (GraphPad Software, Inc, La Jolla, CA). Figures were prepared using Adobe Illustrator CS6. Topological information data analysis. The present analysis is based on the information cohomology framework developed by Baudot and Bennequin 40 that characterizes uniquely entropy and multivariate mutual information (I k ) and develops the topological computational framework for the data analysis  41.
Computation and algorithm. The Information Topology open source program, written in Python, is available on Github depository. It allows to compute the information landscapes, paths, and minimum free energy complex, which encode and represent directly all the basic equalities, inequalities, and functions of information theory  41  , and all the structures of the statistical dependences within a given set of empirical measures (up to the approximations, computational tractability and finite size biases, see previous sections). It can be run on a regular personal computer up to k = n = 21 random-variables in a reasonable time (3 hours), and provides new tools for pattern detection, dimensionality reduction, ranking and clustering based on a unified homological and informational theory.
Acknowledgements This work was funded by the  French National Research Agency (ANR JCJC  grant  ROBUSTEX  to J. M. G. ; supporting S. T. ), the  European Research Council  ( ERC consolidator  grant  616827 CanaloHmics  to J. M. G. ; supporting M. T. P. , P. B. and M. L. ), and the  French Ministry of Research (doctoral fellowship  to M. A. D. ). We would like to thank  Pr. E. Marder ,  Dr. T. O'Leary ,  Dr. M. Seagar ,  Dr. F. Tell ,  Dr. D. Debanne  and  Dr. F. Rico  for helpful discussions on the manuscript.
First, if all members of a sample have taken the same set of diverse cognitive tests, then a data reduction procedure (such as principal components analysis (PCA) or factor analysis) can be applied. Typically, this finds that all tests load on (i. e. , correlate positively with) the first unrotated component, or factor, and scores on this component can be calculated for each person; this gives each person a g score. Second, some mental tests -usually those involving complex mental work, and often those with a variety of item types-have a high g loading  2. That is, scores on some individual cognitive tests can be used to obtain an acceptable proxy for general cognitive ability. An example of the latter is the Moray House Test of verbal and numerical reasoning, which has a high correlation with a PCA-derived general cognitive function score  6.
2a , Supplementary Fig. 2a , and Supplementary Data 1 and 2. There were 434 'independent' significant SNPs; see Methods section for description of independent SNP selection criteria, distributed within 148 loci across all autosomal chromosomes. Note that, for consistency, we use the term 'independent' here according to the definition that is used in the relevant analysis package. A comparison of these 148 loci with results from the largest previous GWASs of cognitive function  16  , and educational attainment  24  , and an MTAG analysis of cognitive function  17  -all of which included a subsample of individuals contributing to the present study-confirmed that 11 of 18, 24 of 74, and 89 of 187 of these were, respectively, genome-wide significant in the present study (Supplementary Data 3).
Of the 148 loci found in the present study, 58 have not been reported previously in other GWA studies of cognitive function or educational attainment (novel loci are indicated in Supplementary Data 4). One hundred and seventy-eight lead SNPs were identified within these 148 loci. For the 434 independent significant SNPs and tagged SNPs, a summary of previous SNP associations is listed in Supplementary Data 5. They have been associated with many physical (e. g. , BMI, height, weight), medical (e. g. , lung cancer, Crohn's disease, blood pressure), and psychiatric (e. g. , bipolar disorder, schizophrenia, autism) traits. Of the 58 new loci, we highlight previous associations with schizophrenia (2 loci), Alzheimer's disease (1 locus), and Parkinson's disease (1 locus). We sought to identify independent significant and tagged SNPs within the 148 significant genomic risk loci associated with general cognitive function that are potentially functional (Fig.
(Fig. 3b, c ; Supplementary Table  1 ; Supplementary Data 8). The significance of this relationship was greatest in the cerebellum and the cortex. 2 ). Genetic correlations for general cognitive function amongst these cohorts, estimated using bivariate GCTA-GREML, ranged from r g = 0. 88 to 1. 0 (Table  2 ). These results indicate that the same genetic variants contribute to phenotypic differences in general cognitive function across each of these three samples. We investigated the genetic contribution to the stability of individual differences in people's verbal-numerical reasoning, by examining data from those individuals in UK Biobank who completed the test on two occasions (mean time gap = 4. 93 years). We found a significant and perfect genetic correlation of r g = 1. 0 (SE = 0. 02). Polygenic profile scores and genetic correlations.
Methods Participants and cognitive phenotypes. The present study includes 300,486 individuals of European ancestry from 57 population-based cohorts brought together by the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), the Cognitive Genomics Consortium (COGENT) consortia, and UK Biobank (Supplementary Note 2). All individuals were aged between 16 and 102 years. Exclusion criteria included clinical stroke (including self-reported stroke) or prevalent dementia (Supplementary Data 18). General cognitive function, unlike height for example, is not measured the same way in all samples. Here, this was mitigated by applying a consistent method of extracting a general cognitive function component from cognitive test data in the cohorts of the CHARGE and COGENT consortia; all individuals were of European ancestry (Supplementary Note 1).
The third UK Biobank sample (VNR MRI) consists of participants who did not complete the verbalnumerical reasoning test at a previous testing occasion but did complete the test at the imaging visit in assessment centres (n = 3002). The fourth UK Biobank sample (VNR Web-Based) consists of participants who did not complete the verbalnumerical reasoning test at any assessment centre visit, but did complete this test during the web-based cognitive assessment online (n = 46,322). Details of the cognitive phenotypes for all cohorts can be found in Supplementary Note 1. At the baseline UK Biobank assessment, 496,790 participants completed the reaction time test. Details of the test can be found in Supplementary Note 1. A sample of 330,069 UK Biobank participants with scores on both the reaction time test and genotyping data was used in this study.
Genome-wide association analyses. Genotype-phenotype association analyses were performed within each cohort, using an additive model, on imputed SNP dosage scores. Adjustments for age, sex, and population stratification were included in the model for each cohort. Cohort-specific covariates-for example, site or familial relationships-were also fitted as required. Cohort-specific quality control procedures, imputation methods, and covariates are described in Supplementary Data 19. Quality control of the cohort-level summary statistics was performed using the EasyQC software  65  , which implemented the exclusion of SNPs with imputation quality <0. 6 and minor allele count <25. General cognitive function meta-analysis. A meta-analysis including all the CHARGE-COGENT and UK Biobank summary results was performed using the METAL package with a sample-size weighted model implemented (http://www.