dataesr/scientific-paragraphs-categorization huggingface dataset:
The samples were assayed for shearing on an agarose gel, and, if the distribution was acceptable (peak distribution ∼400 nt), the samples were sizeselected by solid-phase reverse immobilization (SPRI)/reverse-SPRI purification as described in ref. 55. The fragments then were blunted, and p5/p7 adaptors were ligated, followed by fill-in and gap repair (New England Biolabs). Then each sample was quantified by quantitative PCR (qPCR) using SYBR green and Kapa Hifi. The results were used to determine how many cycles to amplify the resulting library for barcoding using P5-sol and P7-sol primers. The resulting individual libraries were quantified by NanoDrop (Thermo Scientific) and pooled. The resulting library was quantified by qPCR and an Agilent TapeStation, and run on MiSeq 2 × 150. Data were analyzed to confirm AGR conversions and to identify off-target mutations using Millstone, a webbased open-source genome resequencing tool.
Sequences are available online at https://github. com/churchlab/agr_recoding. NNN-Sequencing and CRISPR. CRISPR/Cas9 was used to deplete the wild-type parental genotype by selectively cutting chromosomes at unmodified target sites next to the desired AGR codon changes. Candidate sites were determined using the built-in target site finder in Geneious proximally close to the AGR codon being targeted. Sites were chosen if they were less than 50 bp upstream of the AGR codon and could be disrupted with synonymous changes. If multiple sites fulfilled these criteria, the site with the lowest level of sequence similarity to other portions of the genome was chosen. Oligos of a length of ∼130 bp were designed for all 14 genes with an AGR codon in the first 30 nt after the translation start site. Those oligos incorporated both an NNN random codon at the AGR position and multiple (up to six) synonymous changes in a CRISPR target site at least 50 nt downstream of an AGR codon.
CRISPR repeat-spacer plasmids carrying guides designed to target the chosen sites were electroporated into each diversified pool after the last recombineering cycle. After 1 h of recovery, both the SpCas9 and repeat-spacer plasmids were selected for and passaged in three parallel lineages for each of the 24 AGR codons for 144 h. After 2 h of selection, and at every 24-h interval, samples were taken, and the cells were diluted 1/100 in selective medium. Each randomized population was amplified using PCR primers allowing specific amplification of strains incorporating the CRISPR-site modifications. The resulting triplicate libraries for each AGR codon then were pooled and barcoded with P5-sol and P7-sol primers and run were on a MiSeq 1 × 50. Data were analyzed using custom Matlab code available on https://github.
ACKNOWLEDGMENTS. Stephanie Yaung  generously provided CRISPR plasmids and technical support. This work was supported by  US Department of Energy  Grant  DE-FG02-02ER63445 ; by  US Defense Advanced Research Projects Agency  Grant  N66001-12-C-4211  (to F. J. I. and D. S. ); by  National Institute of General Medical Sciences  Grant  M22854-42  (to D. S. ); by  US Department of Defense National Defense Science and Engineering Graduate Fellowships  (M. J. L. and G. K. ); by a  US National Science Foundation Graduate Research Fellowship  (D. B. G. ); by the  Lynch Foundation  (M. L. ); by an  Amazon Web Services   in Education Grant Award  (G. K. ); and by the  Arnold & Mabel Beckman Foundation and DuPont, Inc. (F. J. I. ). Funding for open access was provided through  US Department of Energy  Grant  DE-FG02-02ER63445.
Introduction The awareness of the behavior of some meteorological parameters is essential for the conception, analysis, development and optimization of solar energy conversion systems, either photovoltaic (PV), concentrated solar power (CSP) or concentrated photovoltaic (CPV) systems. Accurate assessment of meteorological data for long-term prediction is the basis of decision making of banks and investors. Standard numerical simulations of solar energy conversion systems such as System Advisor Model or PVsyst require one year of meteorological data. Methodologies for the generation of Typical Meteorological Years (TMY) datasets representatives of the long-term solar resource of a given site have therefore been developed. A TMY is a customized weather dataset of one-year of meteorological data that aims at representing climatic conditions deemed to be typical over a long-term period.
The most common method for solar energy conversion systems was proposed in 1978 by the Sandia National Laboratory  [1] , slightly modified by several researchers and well detailed in 2008 by Wilcox and Marion  [2]. The resulting TMY dataset is composed of meteorological data of 12 calendar months selected from individual years from long-term record and concatenated to form a complete year, keeping the consistency across the different meteorological variables. In order to select the typical months in the long-term dataset, the method performs a specific weighted combination of the global, diffuse horizontal and direct normal irradiances (GHI, DHI and DNI), air temperature (Tamb), wind speed (WS) and relative humidity (DWT). Monthly data blocks are selected based on the smallest Filkenstein-Schafer (FS) distance measuring the difference of two cumulative distribution functions (CDF).
In 2012, a new approach was proposed in the framework of the European project FP7 ENDORSE  [3]. It introduced the concept of "driver" that is defined by the user as an explicit function of the pyranometric and meteorological relevant variables to improve the representativeness of the TMY datasets with respect the specific solar energy conversion system of interest  [4]. Similarly to the previous method, it calls upon the FS distance. The overarching aim of this study is to benchmark the classical Sandia method with innovative methods based on the driver concept, in the particular case of a given CPV system in a given site.
Reference data used and CPV system used The selected site is the meteorological station Desert Rock, Nevada, in the United States, which belongs to the Surface Radiation (SURFRAD) network  [5]. SURFRAD has adopted the measurement standards set by the Baseline Surface Radiation Network (BSRN). The data exploited here originates from high quality measurements with a 1-min resolution over an 18-year period from 1998 to 2015. The following meteorological variables were recovered from the SURFRAD network: GHI, DHI, DNI, DWT, Tamb, WS and atmospheric pressure. An algorithm is used to check the quality of the meteorological data. Gap filling is performed to provide complete records, by the means of interpolation and mathematical formulas that link the various components of irradiance (GHI, DHI, DNI…).
Methodology In this study, five types of TMY were generated with the following methods: • TMY Sandia: meteorological year built according to the Sandia National Laboratory methodology. The following meteorological variables on the hourly basis are used with specific weighting factors: GHI, DNI, Tamb, WS and DWT. • TMY simplified driver: in that case, the FS-based method is applied only using DNI as a simple driver for the construction of the typical meteorological year. Two simplified drivers have been created, one using hourly data and another using 1-min data. • TMY filtered driver: in that case, the driver is defined as a function depending not only on the DNI but also on the meteorological and mechanical limitations and constraints of the CPV system. As before two filtered drivers have been considered, one using hourly data and another one using 1-min data.
For example, in the case of a 5-year windows, there are 13 periods of data from  [1998] [1999] [2000] [2001] [2002]  to  [2011] [2012] [2013] [2014] [2015]. Fig. 1  presents the schematic of the methodology used to compare the five TMY methods considering different numbers of years. The energy generation output of the CPV system over the long-term 18-years period is the quantity used for the comparison between the different TMY datasets. This energy output has been chosen since it is directly related to the profitability of the system and thus to its bankability and its associated decision making. A simulation tool was then used with each TMY as input to calculate the yield of the CPV system. This tool is an internal tool developed and validated by SOLAÏS. In order to assess the relevance of the TMY datasets generated with each methodology and each historical period, two analyses are performed: one comparing the long-term average yield with the yield associated to the TMY datasets and another comparing the corresponding CDFs through the Kolmogorov-Smirnov test Integral parameter (KSI)  [7].
Performance indicators Firstly, the monthly yield obtained from each meteorological year per historical periods is compared to the monthly yield of the reference long-term 18-year period. The maximum absolute values of monthly deviation per number of years of the historical period and per method of TMY generation corresponds to the worst-case for each method. Fig. 2a  illustrates the procedure followed for this analysis. The same analysis is applied to the annual yield. Secondly, the KSI test is used to compare the CDF of the long-term yield to the CDF of the yield resulting from the TMY datasets. For that purpose, hourly production data is used. Both monthly and annual data is analyzed. Fig. 2b  presents the procedure followed for this analysis. The maximum values of KSIr per number of years of the historical period and per method of TMY generation corresponds to the worst-case for each method.
Monthly averages The monthly average yield is calculated for the 18 years used as the long-term period of reference. The results are presented in Table  1. The monthly values are considered as the reference to be compared with the values obtained from the different TMY datasets used as inputs to the CPV yield simulations. Fig. 3  shows the worst case per number of years of the historical period as explained in section 3. 1. The results at the monthly basis obtained from the drivers, with hourly and 1-min data, are significantly better than those obtained with the Sandia method. The deviations obtained with the drivers, for the worst month, are reduced up to 77 % compared with those obtained with the Sandia method. The results for the simplified and filtered drivers are equivalent with stable results between 6-and 13-year data, and significant improvements from 14-year data, with a deviation below 6.
Conclusions and perspectives The results obtained with the analysis at the monthly basis, compared with the average long-term yield so as with the application of the KSI test, indicate that the TMY datasets generated with the simplified and filtered drivers are more representative to the long-term energy production than those generated with the Sandia method for CPV systems at the site of study. The lack of representativeness at the monthly basis observed for the Sandia method seems to balance over the year. Thus, the annual results are close to the long-term reference behavior. However, the performances obtained with the simplified and filtered drivers are better than those obtained with the Sandia method. Regarding the average yield, an annual deviation less than 1. 7 % was found with the simplified and filtered drivers from 9-year data, whereas the Sandia method yields greater deviations.
The TMY dataset obtained using dedicated drivers are thus more representative to derive TMY from limited long-term meteorological dataset. The added value of the non-linear behavior brought by the filtered driver is not significant compared to the simplified driver in this particular hypothetical study case. In addition, the difference between the TMY datasets obtained using dedicated drivers with 1-min data and hourly data are not significant. The simplified driver using hourly data seems a good compromise to obtain a typical meteorological year, due to its simplicity and good performance of the resulting TMY. The present conclusions are applicable to Desert Rock site, for which the historical time series of wind speed does not have a significant impact on the tracking system performance.
Therefore, efficient routing techniques are highly required to balance the energy consumption among the network nodes and maximize the network lifetime  [5]   [6]. Different routing protocols for WSN proposed in literature are categorized based on their computational complexity, network structure, energy efficiency and path establishment  [7] [8]  [9]. One of the principal classes is hierarchical routing protocols. In this class, the main idea is that every sensor node within a WSN is grouped along with some other of its neighboring nodes in order to constitute a specific cluster. Clustering provides an effective method for prolonging the lifetime of a WSN. Data, collected by the sensor nodes belonging to a cluster, are not directly transmitted to the Base Station (BS). Instead, a node of the cluster, called CH, collects these data and forwards them to the BS after possibly having performed appropriate data aggregation.
Highlight by Daniel Gautheret Quantifying gene expression from RNA-Sequencing (RNA-Seq) data used to be a settled matter. Nearly all protocols would start with "Step 1: align sequence reads onto a reference genome". This process required days of computer time and access to powerful servers, even for a basic experimental setup. As RNA-Seq projects grew larger in scale, running this analysis started to turn into a bioinformatician's nightmare. This is now changing radically thanks to efforts by two US-based groups. Their new programs, Kallisto  [1]  and Salmon  [2] , are based on hashing, a method that converts a sequence file into an index of words of fixed size (k-mers). Comparing an RNA-Seq library to an entire transcriptome at the k-mer level now takes minutes instead of hours. Both programs implement procedures to resolve ambiguities (k-mers matching several transcripts) and assign a read count to each transcript, achieving a precision in transcript abundance estimation comparable to that of the best mapping-based methods.
Setting Computation are performed using GraphX  [25]  APIs in Spark  [27]  (version 1. 3. 1), on a 16 nodes cluster. Each machine has 22 cores with 60 GB RAM running Linux OS. For our experiments we set teleporting probability α to a classical value 0. 15. The depth of the BFS exploration (i. e. , the maximum length considered for paths from seed to other vertices) is set to 4. So we intend to compute only the nearby vertices to current seed, rather than to every vertices in graph, according to Six Degrees of Separation theory  [17]. Data Sets. We validate our approach on two datasets: LiveJournal  [6]  with 4. 8M vertices and 68. 9M edges, and Pokec  [22]  with 1. 6M vertices and 30. 6M edges. These datasets can be downloaded from SNAP 1.
Runtimes We propose to evaluate how the runtime of different graph processing algorithms benefits our partitioning, compared to other methods. First, we launch FMRW, a heavy-communication algorithm, on LiveJournal and Pokec datasets respectively, with 3 random walks of length 4 started from each vertex. From the results in Figure  4 , we see that our partitioning can save up between from 20 to 65 percent of runtime, compared with other partitionings, for both datasets. We also test our method with traditional PageRank algorithm. We consider the static (fixed number of iterations) and dynamical (with convergence and a threshold value) approaches. We consider there are 200 partitions and we proceed to resp. 30, 50 and 100 iterations for static PageRank and to dynamical PageRank with resp. 0.
Acknowledgements. We thank  Joe Prospero  for access to his measurement station at Ragged Point. We are grateful to  Stephan Henne ,  EMPA  ( Swiss Federal Laboratories for Materials Science and Technology ), for kindly providing the 10-day back trajectories (to be found at http://lagrange. empa. ch/FLEXTRA_browser). We acknowledge the use of FIRMS data and imagery from the  Land Atmosphere Near-real time Capability  for EOS (LANCE) system operated by the  NASA/GSFC/Earth Science Data and Information System (ESDIS)  with funding provided by  NASA/HQ. This project was partly funded by the  DFG  ( SI 1534/3-1 ) and the  European FP7  project  BACCHUS (Impact of Biogenic vs. Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding , grant agreement no. 49 603445 ). Thanks also to  David Farrel  from the  Caribbean Institute for Meteorology and Hydrology (CIMH)  for logistical support.
Generation of clusters was performed on Illumina cBot following standard protocols except that the sequencing primer was replaced by 5 0 -ACA CTCTTTCCCTACACGACGCTCTTCC-3 0 (ref. 68). Sequencing was performed on Hiseq 1000 sequencer (single read 51 pb) according to standard procedures. Sequencing data. All Illumina sequencing runs were performed at IMAGIF high-throughput sequencing platform. Sequence reads were identified and demultiplexed using the standard Illumina software suite and adaptor sequences were removed by Cutadapt (version 1. 2. 1). Reads 410 nt were aligned to the human reference genome (hg19) using the BWA (version 0. 7. 5a) software with default parameters. We considered only uniquely mapping reads and removed PCR duplicate reads using Picard (http://picard. sourceforge. net).
92 (Pearson), Po10 À 15 (t-test) and for GM06990 cells, R ¼ 0. 93, Po10 À 15 ). Similar correlations were observed between EdU and EdC profiles. Segmentation of RFD profiles. A four-state HMM was used to detect within the RFD profiles the AS, DS and FS segments representing regions of predominant initiation ('Up' state), predominant termination ('Down' state) and constant RFD ('Flat1' and 'Flat2' states; see Supplementary Fig. 9a  for a graphic representation). The RFD values were computed within 15 kb sliding windows (stepped by 1 kb across the autosomes). The HMM used the DRFD values between adjacent windows (that is, DRFD n ¼ (RFD (n þ 1) À RFD (n) )/2 for window n). Windows with o30 reads on one strand were masked. The DRFD values were divided into five quantiles and the HMM package of R (http://www.
25. Nucleotide compositional skew profile. The nucleotide compositional skew (GC and TA skews defined as S GC ¼ (G À C)/(G þ C), S TA ¼ (T À A)/(T þ A) and total skew S ¼ S GC þ S TA ) was computed in 1 kb adjacent windows with the repeat masked sequences as described before  19, 24  and the coordinates of skew N-domains were retrieved from ref. 19. Comparison with transcription data. Annotations of the human genome (version 73) were retrieved from the Ensembl Genome Brower (http://www. ensembl. org). The gene transcription level of each cell type was obtained from the Expression Atlas (www. ebi. ac. uk/gxa), which uses the transcription data generated by the ENCODE project to compute the average FPKM (fragments per kilobase per million reads) using biological replicates. The raw RNA-sequencing data (PolyA þ , Long PolyA À and short Poly A À ) were downloaded from the UCSC genome browser.
Published HeLa SNS-seq  11  , HeLa ORC1 ChIP-seq  13  and GM06990 bubbles 4 data (detected peaks) were used to compute enrichments along AS and surrounding regions in adjacent 1 kb windows. To compare the enrichments, the density profiles were normalized by the average density within the examined region (Fig. 6a ,b, Supplementary Figs  12 13 14 15 16 ). In Supplementary Fig. 13a-c , raw HeLa SNS-seq  9  and ORC1 ChIP-seq  13  data were used to compute enrichments around TSS in adjacent 100 nt windows. Histone and open chromatin marks. The data of signal enrichment for histone marks, including H2A. Z, H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K9me3, H3K27ac, H3K27me3, H3K36me3, H3K79me2 and H4K20me1 for HeLa and GM12878 (closely related to GM06990) cells were downloaded from the Board Histone track at the UCSC genome browser.
The data for nucleosome positioning (MNase-seq) in GM12878 cells were downloaded from the Stanf Nucleosome track at the UCSC genome browser. The DNase I-seq data (UW DNase I HS data track) and the DNase I hypersensitivity uniform peaks of HeLa and GM06990 cells were retrieved from ENCODE. Hi-C chromatin interaction data. The Hi-C data of HeLa  69  and GM06990 (ref. 60) cells were retrieved and the Directionality Index (DI) was computed as described  61  : DI ¼ B À A ð Þ = B À A j j ð ÞA À E ð Þ 2 =E þ B À E ð Þ 2 =E À Á ð3Þ where A and B are the number of reads that map from a given 40 kb bin to the upstream and downstream 2 Mb, respectively, and E, the expected number of reads under the null hypothesis (E ¼ (A þ B)/2). The coordinates of the TADs of H1 human embryonic stem cells and IMR90 fibroblasts were retrieved from ref.
Acknowledgements We thank  Arach Goldar  ( CEA-Saclay ) for helpful discussions. This work has received support from the  Fondation pour la Recherche Medicale (Equipe labellisee) , the  Ligue Nationale Contre le Cancer (Comitede Paris) , the  Canceropole Ile-de-France (ERABL) , the  Agence Nationale pour la Recherche  ( REFOPOL-BLAN2010-161501 ), the  programme 'Investissements d'Avenir ' launched by the  French Government  and implemented by the  ANR  ( ANR-10-LABX-54 MEMO LIFE ANR-11-IDEX-0001-02 PSL* Research University ) and the  Centre National de la Recherche Scientifique. This work has benefited from facilities and expertise of the  IMAGIF sequencing platform  ( Centre de Recherche de Gif  -http://www. imagif. cnrs. fr).
7 (3σ, 3 , AB magnitude). Through a comparison with the zCOSMOSbright spectroscopic redshifts, we measure a photometric redshift precision of σ ∆z/(1+zs) = 0. 007 and a catastrophic failure fraction of η = 0. 5%. At 3 < z < 6, using the unique database of spectroscopic redshifts in COSMOS, we find σ ∆z/(1+zs) = 0. 021 and η = 13. 2%. The deepest regions reach a 90% completeness limit of 10 10 M to z = 4. Detailed comparisons of the color distributions, number counts, and clustering show excellent agreement with the literature in the same mass ranges. COSMOS2015 represents a unique, publicly available, valuable resource with which to investigate the evolution of galaxies within their environment back to the earliest stages of the history of the Universe. The COSMOS2015 catalog is distributed via anonymous ftp b and through the usual astronomical archive systems (CDS, ESO, IRSA).
Taking these considerations into account, it is clear that new observational studies will require deep, nearinfrared (NIR)-and infrared (IR)-selected data. This will allow us to extend stellar mass measurements and photometric redshift catalogs to higher redshifts and lower stellar masses over the largest possible redshift range. In particular, the challenge is to cover simultaneously in the same dataset the low-mass and high-redshift ranges of the galaxy population. Especially the redshift range 1 < z < 4 where galaxies are most actively forming stars. As most spectral features move into the rest-frame optical in these redshift ranges, NIR data is essential for accurate photometric redshift and stellar mass estimates. Covering a large area is also essential to derive robust statistical N -point functions or count in cells, to probe a variety of galaxy environments, to trace accurately the large-scale structure, and to minimize the effect of cosmic variance.