dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq The RNA-seq data regarding proliferation/quiescence/dif ferentiation of the 3T3-L1 preadipocyte cell line have been described previously  (4)  and are available at the NCBI GEO web site under accession number GSE50612. Total RNA was prepared 16 hours after Min6 cell transfection as above. Strand-specific libraries were prepared using Illumina's TruSeq Stranded mRNA Sample Preparation kit from four independent replicates of Plagl1-and NLS-eGFP-transfected Min6 cells and sequenced on a HiSeq 2500 (single reads 50 nt) as described  (4). Image analysis and base calling were performed using the HiSeq Control Software and Real-Time Analysis component. Demultiplexing was performed using Illumina's Conversion Software (bcl2fastq 2. 17). The quality of the data was assessed using FastQC v0.
11. 5 from the Babraham Institute and the Sequence Analysis Viewer (Illumina). Potential contaminants were monitored with the FastQ Screen software from the Babraham Institute. We aligned RNA-seq reads to the mouse genome (UCSC mm9) with the splice junction mapper TopHat 2. 1. 1, which used Bowtie 2. 2. 8. We downloaded gene model annotations from the UCSC database (genes. gtf 15 January 2012) and GeneIDs from the NCBI (gene2refseq. gz, 19 January 2012). Final read alignments having more than 3 mismatches were discarded. We performed gene counting using the union mode of HTSeq-count 0. 6. 1p1. Before statistical analysis, genes with less than 20 reads (cumulating the eight analyzed samples) were filtered out. Counts were normalized using the Relative Log Expression (RLE) method as implemented in edgeR  (20) , which was used to identify differentially expressed genes with a generalized linear model likelihood ratio test using a paired design.
We corrected Pvalues for multiple testing using the Benjamini-Hochberg FDR method. Genes with adjusted P-value <0. 01 were declared 'differentially expressed'. RNA-seq data from this study have been submitted to the NCBI GEO under accession number GSE84914. We analyzed the transcriptome of two independent MEF preparations from Plagl1 +/-pat. and control littermate E13. 5 embryos. MEF RNA-seq libraries were prepared and sequenced as above. Data were processed as above with the following changes: bcl2fastq 2. 18, Bowtie2 2. 2. 9, genes with <10 reads were filtered out. Data have been submitted to the NCBI GEO under accession number GSE99409.
The remaining 4638 peaks were annotated with the three closest neighboring genes at a maximal distance of 20 kb using CisGenome. Enriched sequence patterns in Plagl1 binding regions were determined using Gibbs Motif Sampler provided by CisGenome that allowed calculating the Relative Enrichment Levels for these motifs. We assessed the abundance of the consensus motives in Plagl1 binding sites using the Motif Enrichment Analysis Framework  (24). Representative illustrations were made using IGB (Integrated Genome Browser). ChIP-seq data from this study have been submitted to the NCBI GEO database under the accession number GSE75943. ChIP-seq data for Nptx1 and Mpl promoters were confirmed by ChIP-qPCR using primers listed in Supplementary Table  S1. Data were normalized using three loci L1, L2, L3, devoid of Plagl1 binding sites according to the formula: X/geometric mean (L1, L2, L3) = 2 (Ct(X)-arithmetic mean (Ct(L1), Ct(L2), Ct(L3)) , where Ct is the threshold cycle.
We also compared the list of genes bound or regulated by Plagl1 to the genes of the IGN (Table  1 , Supplementary Figure  S8 ). Plagl1 was binding close to 88 of the 407 IGN genes present in the UCSC database (∼22%, P < 2 × 10 -3 , Fisher's exact test). Interestingly, among expressed genes, IGN genes were significantly more frequently regulated by Plagl1 than non-IGN genes (Supplementary Figure  S8 ; 15/297 versus 336/12 564, P < 0. 02, Chi-square test). To further illustrate how Plagl1 binding may contribute to IG regulation we provide maps of consensus and observed Plagl1 binding sites at imprinted loci (Supplementary Figure  S10 ). Interestingly, Plagl1 bound in Neuro-2a 148 (48%) of the 308 genes regulated in Min6 cells only (Supplementary Figure  S6 ).
We also noted the presence of ChIPseq peaks that were not statistically significant in the vicinity of the Nptx1 promoter, as well as a G 4 C 4 consensus binding site. ChIP-qPCR data close to this consensus site suggested that the promoter may indeed contain a weak Plagl1 binding site (Figure  6C ). We cloned 2 fragments of the Nptx1 promoter into pGL3 and measured luciferase activity in CAT-and Plagl1-transfected Neuro-2a cells. We observed a 3-fold transactivation of the shortest fragment by Plagl1 (Figure  6D ). The longer fragment displayed an elevated basal promoter activity, probably due to the presence of sequence from the neighboring Gm11762 gene pro- moter, and a weak 1. 7-fold induction by Plagl1 (Figure  6D ). These data confirmed that Plagl1 directly bound and regulated Mpl and Nptx1 promoters, possibly in combination with other regulatory elements in the case of the Nptx1 gene.
ACKNOWLEDGEMENTS We thank  Aubin Thomas  ( Institut de Génétique Humaine, Montpellier ) for help with bioinformatics,  Hongkai Ji  ( Johns Hopkins University Bloomberg, Baltimore ) for help with the CisGenome software,  Anja Bellmann  for help with quantitative PCR experiments,  Isabelle Janoueix-Lerosey  ( Institut Curie, Paris ) for providing P1 sympathetic ganglia RNA,  Chrystel Lafont  for help with imaging techniques, and  Christophe Duperray  ( MRI-Montpellier Ressources Imagerie ) for help in flow cytometry experiments.
The panel consisted of 20 persons who tested five sensory attributes such as shape, color, aroma, taste and crispness. The color of extrudate cross-sections (Fig. 1 ) was measured with a Digital Image Analysis set and expressed in CIEL*a*b* color system  [15]. Images were acquired with a Nikon DXM-1200 (Nikon Inc. , Melville, USA) chargecoupled device (CCD) color camera. The color parameters were designated using LUCIA G v. 4. 8 software (Laboratory Imaging, Prague, Czech Republic). The light source was a Kaiser RB 5004 HF -High Frequency Daylight Copy Light set with 4 × 36 W fluorescent tubes of 5400 K) (Kaiser Fototechnik GmbH & Co. KG, Germany). The expansion of the extrudates was characterized based on the expansion ratio (the ratio of the diameter of the extrudate to the die diameter) and the longitudinal and radial expansions (the linear length and diameter of extrudates measured with a caliper).
07-0. 33. The extrudates monomolecular layer capacity, the sorption-specific surface, energy constant, total capacity and the size of capillaries were determined  [18]. The results were analyzed using Jandel-Table Curve 2D software v. 5. 01. The fit of the empirical data to the BET equation was characterized by the determination coefficient (R 2 ) and the standard error estimation (FitStdErr) and Fstat statistics. The results of all analyses were analyzed using Statistica 12. 0 PL software (StatSoft, Kraków, Poland). The differences between the means were determined using analysis of variance (ANOVA) at a significance level p = 0. 05 with Tukey's test.
Microscopy and fluorescent pixel quantification Embryos were transferred to glass-bottom dishes (MatTek Corp. , Ashland, MA) in E3 (inverted microscope) or embedded in 0. 5% E3-agarose (confocal) containing 0. 02% MS222. A Leica DM IRB inverted microscope (bright-field, differential interference contrast (DIC), and fluorescence imaging) coupled to a Coolsnap fx camera (Roper Scientific) was used. A Nikon AZ100 equipped for bright-field and fluorescence imaging, coupled with Coolsnap HQ2 (Roper Scientific) using MetaVue software was used to record full size embryos. Confocal microscopy was performed with an Olympus FV10i and images and movies were processed with Fluoview software and Image J. Images were processed further using Adobe Photoshop, and time-lapse videos made with image J (see specific details in movie legends).
Acknowledgments We greatly acknowledge  J. Goedhart  for providing pmTurquoise2-C1,  G. Lieschke  for mpeg1: Gal4/uas-kaede fish,  G. Lutfalla  for mpeg1:mCherry-F and il1b reporter fish,  P. Martin  and  Y. Feng  for L-plastin antibodies, and  S. Renshaw  for mpx:GFP, mpx;Gal4;uas-Kaede and fms: Gal4;umn fish. We thank  Y. Tasrini  for constructing pIN301.
INTRODUCTION The Highway infrastructure is one of the most valuable assets for any state or national Government  (Taggart and Moss, 2015). Efficient operations of Highways lead to success of national and local economies as well as improve the quality of life of the general public dependant on it. In order to ensure aging road networks continues to move with its ever increasing number of users, requires maintenance and improvements to the road network at the highest standard. Increasing scrutiny over the cost of maintenance along with increasing pressure from Government and the public for transparency over road network spending, has made a strong case for more F a c i l i t i e s efficient management of the Highway road asset and traffic management data. Better management of information could allow for life cycle management of asset data and predictive analytics.
The information is subsequently used to create AIM (Asset Information Model) which is used as a single source to store all information related to this project. AIM allows a smooth and easy way to search for any needed information during the operational phase of the asset. Use of BIM in a project lifecycle where multiple stakeholders collaborate and produce vast of data. However, use of BIM in a project lifecycle is not the only data source, other systems (e. g. GIS, ERP etc. ), stakeholders (such as contractors, subcontractors, client, suppliers etc. ), site data collection technologies (such as RFIDs, wireless sensors etc. ) and external data (web streams, social media, open data etc. ) collate vast amount of data which can be utilized in operation and maintenance phase after construction.
All this data creation and usage point to the four dimensions of Big Data: volume, variety, velocity and veracity (IBM, 2014). Data is considered 'Big' when it exceeds the capacity of traditional database management systems, doesn't fit in the structures of database architectures and posing challenges such as scalability, unstructured or semi-structured data, accessibility, real time analytics and fault tolerance  (Satyanarayana, 2015). Managing this Big Data is critical where the Big Data movement seeks to glean intelligence from data  (McAfee and Brynjolfsson, 2012)  and to uncover patterns to guide decision makers using predictive and prescriptive analytics  (Camm et al. , 2015). For example, predictive modeling can be used for distribution of the performance variables to provide future maintenance needs, or to predict a single value for each performance variable and provide the location as well as volume of future maintenance  (Taggart and Moss, 2016).
RESEARCH METHODOLOGICAL YAPPROACH The encompassing theme of this research is emerging field of Big Data Analytics and its applications within Highways context This study employed different methods to provide a multi-faceted view of the problem area, both at specific Highways Scheme level as well as to understand broader issues faced by the industry. A literature review was initially performed to systematically gather information to identify and understand the problem domain. This literature along with discussed case studies became a starting point to understand role of BIM processes and technologies as a unified information exchange mechanisms through the asset life cycle. Data management applications in other industries were also reviewed. Interviews were held with owner and contractors involved in Highways maintenance to develop a better understanding of key challenges.
For instance, a civil engineer involve in road design will be able to analyse design models to quickly determine whether the road geometry meets safety parameters for sight distance. Data integration will allow them to consider not only the road layout, but also overlay this with other information which will impact on sight distance such as the road camber, visual obstructions (such as central barriers) and road-side foliage. Any gaps in the capability of the platform can be overcome by enhancing existing programmes by using plug-ins (e. g. which provide details of new specification) or by using software development kit (SDK) to create bespoke additional features and functionality. Users are able to interface with the platform using applications suitable for their work so project managers may use application such as Primavera within the platform rather than the 3D model orientated software that engineer would prefer.
External information system such as Geographical Information Systems (GIS) is integrated with the BIM server to provide the localization of real time assets of a highway. To take a benefit of the parallel processing that Hadoop provides, query for data extraction will be executed as a MapReduce job. Map Reduce is a distributed data processing model and execution environment that works by breaking the query processing into two phases: the map phase and the reduce phase  (Jach et al. , 2015). Whereas, HBase a Hadoop application, is a distributed column-oriented database built on top of HDFS and is used to query large datasets in real time  (Qiu et al. , 2011). HBase is not relational and does not support SQL, but it is able to do what an RDBMS cannot: host very large, sparsely populated tables created from different data sources on clusters made from commodity hardware  (Lee et al.
5b. Connectivity and data exchanges of BIM objects with Hadoop Cluster First of all, data from all sources that is logically mapped to BIM objects will be uploaded to HDFC via TCP connection. A TCP connection is established to the data server (Hadoop cluster) with the server`s IP address and port number by executing a program written in Java. After a data file is successfully uploaded, the sensor application may move on to upload the next file and this process will keep on going until it reaches the upper limit of files. As soon as number of uploaded files reaches the defined upper limit, the TCP socket will be closed and MapReduce task will be triggered. To extract only the desired sensor data from data files, MapReduce will extract and then insert data to the HBase database system. The sensor data is inserted according to the column name it belongs to.
A dynamic data connection is established between BIM server and Hadoop cluster. Proposed system GUI is invoked as a Revit Add In from Revit Structures GUI, using "External Tools" option. A selfupdating GUI is displayed with real-time sensor data. BIM plays a significant role in this process since the sensor data of the highways is populated with relevant parametric data from the BIM software. The asset manager can select the desired junction using its unique ID which will display its BIM model and information from the embedded sensors in that location. Establishing a data link that uniquely ties sensed data to a highway junction is all that is required to pair collected data to the space that it describes. Asset managers can connect to BIM server either by standard web browser or an Android Application that is developed to visualize the collected data from the data server.
5c. Prototype Mobile Application To keep updated with the acquired data, system should not only manage data acquisition from sensors but also synchronization and provide GUI to asset managers to remotely monitor data of a motorway and its assets. In order to do so, an Android based application is necessary. An Android based platform is chosen because of its free, open source and powerful software development kit  (Janpla et al. , 2015). Android prototype application, as part of the proposed platform, is designed using Eclipse and it is it is tested in Android Virtual Device (AVD). Eclipse is an integrated development environment (IDE) contains a base workspace and an extensible plug-in system for customizing the environment  (Chopvitayakun, 2015). Written mostly in Java, Eclipse is used to develop applications.
DISCUSSION AND CONCLUSIONS Building Information Modelling (BIM) supported by Big Data analytics has the potential to is revolutionizeing the way assets are conceived, designed, built and operated  (Davis and Sharp, 2014). The success of BIM, however, depends on the effective integration of its three components: people, process and technology. This paper focused more on technology aspects related to life cycle data management. More specifically, it is concerned with proposing a platform that integrates the various technologies and systems of Highways Authority and its supply chain, to allow for continuous flow of data throughout an asset's life-cycle, leading to seamless, collaborative and effecting working. The developed prototype application investigates the integration of BIM with Hadoop framework for Highway road asset and traffic data management.
Will it be worth the investment? What should we anticipate in that sense? If those critical matters are not addressed properly, the discussion on Big Data will remain elusive for the industry. Considering the industry is quite hard benefit oriented and practical, if one falls short in demonstrating the value of Big Data, It will be hard to get the industry's real buy-in to turn conceptual architectures/ mock models into working practices. It is more about the analytics and identifying the unseen trends than the database systems and data collection means. It is an area with potential for the maintenance of assets. Thank you very much for the interesting paper. A thorough proof-reading is highly recommended. Many grammatical Paper has been thoroughly read to address grammatical errors and F a c i l i t i e s errors and typos were identified.
Acknowledgments These protocols are adapted from previous work reported by our group (Trajtenberg et al. , 2016). We are grateful to the staffs at synchrotron beamlines Proxima I and II, Soleil (France), especially  William Shepard ; and  Daniela Albanesi  for providing plasmid pHPKS/Pxyl-desKSTA. This work was supported by grants from  Agencia Nacional de Investigación e Innovación (ANII) ,  Uruguay  ( FCE2009_1_2679 ; FCE2007_219 );  Agence Nationale de la Recherche (ANR) ,  France  ( PCV06_138918 );  Centro de Biología Estructural del Mercosur  (www. cebem-lat. org) and  Fondo para la Convergencia Estructural del MERCOSUR  ( COF 03/11 ). We are also grateful to the  Institut Pasteur International Network  for institutional support through the  IMiZA International Joint Unit. Copyright Imelio et al.
Software 1. Produced diffraction datasets: a. Complex DesKC:DesR-REC in phosphotransferase state, low Mg 2+ : http://dx. doi. org/10. 15785/SBGRID/399 b. Complex DesKC:DesR-REC in phosphotransferase state, high Mg 2+ : http://dx. doi. org/10. 15785/SBGRID/401 c. Complex DesKC:DesR-REC in phosphotransferase state, high Mg 2+ and BeF3 -: http://dx. doi. org/10. 15785/SBGRID/408 d. Complex DesKC:DesR-REC in phosphatase state: http://dx. doi. org/10. 15785/SBGRID/400 Copyright Imelio et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4. 0). www. bio-protocol. org/e2510 Vol 7, Iss 16, Aug 20, 2017 DOI:10. 21769/BioProtoc. 2510 6 2. Produced atomic coordinate models: a. Complex DesKC:DesR-REC in phosphotransferase state, low Mg 2+ : http://www. rcsb.
org/pdb/explore/explore. do?structureId=5IUJ b. Complex DesKC:DesR-REC in phosphotransferase state, high Mg 2+ : http://www. rcsb. org/pdb/explore/explore. do?structureId=5IUK c. Complex DesKC:DesR-REC in phosphotransferase state, high Mg 2+ and BeF3 -: http://www. rcsb. org/pdb/explore/explore. do?structureId=5IUL d. Complex DesKC:DesR-REC in phosphatase state: http://www. rcsb. org/pdb/explore/explore. do?structureId=5IUN 3. Computational crystallography software utilized: a. autoPROC (https://www. globalphasing. com/autoproc) b. BUSTER (https://www. globalphasing. com/buster) c. CCP4 (http://www. ccp4. ac. uk) d. Phaser (http://www. phaser. cimr. cam. ac. uk) e. Coot (https://www2. mrc-lmb. cam. ac. uk/personal/pemsley/coot) f. MolProbity (http://molprobity. biochem. duke. edu) g. PyMol (https://www.
PC1   1. Maps were drawn with SURFER v. 12 (Golden Software, Golden CO, USA). coordinates were highly significantly different by subhaplogroup (p ~ 10 -150 , ANOVA). The median haplotype for Z220-derived chromosomes was 11-14-18 at YGATAH4-DYS437-DYS448 while it was 12-15-19 for the rest of DF27 chromosomes (other STRs showed the same median allele). PC2 explained 8. 6% of the STR variation and correlated with DYS390 (r = 0. 647) and DYS456 (r = -0. 544), and separated R1b-Z220* from R1b-Z278* chromosomes; overall, R1b-DF27 subhaplogroups had significantly different PC2 coordinates (p = 9. 12 × 10 -4 ). The same PC results can also be analyzed by population (Fig. 4b ): PC1 coordinates are statistically significantly different by population (ANOVA, p = 0. 00512), with the French samples having higher, positive values in this  PC, while PC2 is not significant across populations (p = 0.
2 and Z220 have similar distributions, as M167 and Z278 have as well; finally, M153 shows the lowest pairwise distribution values. This pattern is likely to reflect the respective ages of the haplogroups, which we have estimated by a modified, weighted version of the ρ statistic (see Methods). We estimated an age of 4190 ± 140 ya for the whole of DF27. This figure is remarkably similar both to the estimate (4128 ± 71 ya) that can be produced from whole Y-chromosome sequence variability in the 88 DF27-derived individuals present overall in the 1000 genomes project dataset, and to the age estimated from 201 individuals in our dataset for which 21 non-duplicated Y-STRs from the Powerplex Y23 System were available  26  (3880 ± 165). Z195 seems to have appeared almost simultaneously within DF27, since its estimated age is actually older (4570 ± 140 ya).
The Basques always produce lower estimates, even for M153, which is almost absent elsewhere. R1b-DF27 and demography. We tested the dynamics of R1b-DF27 by means of Approximate Bayesian Computing (ABC). In particular, we compared two simple models: constant population size vs. growth since time Tstart. Both the rejection and the regression method undoubtedly favoured the growth model, with associated posterior probabilities that were never lower than 0. 99. The principal component analysis (PCA) of the first 3,000 best simulations from each model (i. e. the 3,000 simulation closest to the observed dataset that are generated by each model) actually shows that the point corresponding to the observed data falls in the middle of the results obtained simulating growth, thus confirming that the growth model is also able to generate the observed variation (Suppl.
Discussion We have characterized the geographical distribution and phylogenetic structure of haplogroup R1b-DF27 in W. Europe, particularly in Iberia, where it reaches its highest frequencies (40-70%). The age of this haplogroup appears clear: with independent samples (our samples vs. the 1000 genome project dataset) and independent methods (variation in 15  Table  3. Haplogroup ages estimated from STR variation with the weighted ρ method. 95% CI: 95% confidence interval. around 4000-4500 ya, which coincides with the population upheaval in W. Europe at the transition between the Neolithic and the Bronze Age  2, 9. Before this period, R1b-M269 was rare in the ancient DNA record, and during it the current frequencies were rapidly reached  2, 9, 10. It is also one of the haplogroups (along with its daughter clades, R1b-U106 and R1b-S116) with a sequence structure that shows signs of a population explosion or burst  1.