dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012; Van Niel et al. , 2012). These methods have been reviewed and compared in different studies with the purpose of identifying the most robust ET i to ET d upscaling approach based on different datasets, time integrals, and varying sky condi-tions  (Cammalleri et al. , 2014; Ryu et al. , 2012; Tang et al, 2013 Tang et al, , 2015;; Van Niel et al. , 2012; Xu et al. , 2015). Based on the previous studies, we find that the R S TOA approach performed consistently well at lower temporal resolution, namely 8-day to monthly scales  (Ryu et al. , 2012; Van Niel et al. , 2012) , as well as under clear-sky conditions  (Cammalleri et al. , 2014) , whereas the R S approach was identified as the most preferred method for ET i to ET d conversion at a higher temporal scale, i. e. daily timescale in addition to under variable sky conditions  (Cammalleri et al.
Given the information of cloudiness is also obtainable from remote sensing, we consider R S to be a robust variable to explore ET i upscaling. Even though this study is intended for remote sensing application, we tested the method using meteorological and surface energy balance flux measurements from eddy covariance (EC) system at the FLUXNET  (Baldocchi et al. , 2001)  sites mainly for the purpose of temporal consistency. However, we evaluate the performance in consideration with overpass time of polar orbiting satellites commonly used in operational ET mapping, namely MODIS and LANDSAT. By choosing to use data distributed over different ecosystems and climate zones, we are faced with two problems: (1) changing cloud conditions across ecosystems and (2) varying energy balance closure (EBC) requirements for the fluxes in different ecosystems  (Foken et al.
, 2006; Franssen et al. , 2010; Mauder and Foken, 2006; Wilson et al. , 2002). Currently, information on cloudiness is obtainable from geostationary meteorological satellites, at hourly to 3 h time steps, e. g. from the Clouds and Earth's Radiant Energy System (CERES), the International Satellite Cloud Climatology Project-Flux Data (ISCCP-FD), and Global Energy and Water cycle Experiment Surface Radiation Budget (GEWEX-SRB). The CERES algorithm uses cloud information from MODIS onboard both Terra and Aqua platforms and combines it with information from geostationary satellites to accurately capture the diurnal cycles of clouds. In this study, cloudiness is not included in the list of variables used to estimate R Sd due to inconsistency in spatial resolution of data to match with the other predictive variables used.
Including cloudiness holds great potential for improving the ANN R Sd predictions due to their direct relationship  (Mallick et al. , 2015). However, we assess the performance of the ANN under cloudy sky conditions based on simple cloudiness index computations as adopted from previous works  (Baigorria et al. , 2004). The EBC problems have been reported to vary across landscapes due to management practices, climate, seasons, and plant functional type characteristics  (Foken et al. , 2006). In this study, in order to test the robustness of the proposed method, we initially disregard the sitespecific EBC problems and assumed that the systematic bias of fluxes fall within the same range across entire FLUXNET database used. The objectives of the present study are: (1) using a ANN with Multilayer Perceptron (MLP) architecture to predict R Sd based on R Si satellite observations, (2) applying the R Sd / R Si ratio as a scaling factor to upscale ET i to ET d under all sky conditions, and (3) comparing the performance of the proposed R S -based ET i upscaling method with R S TOA and EF-based ET i upscaling methods across a range of temporal scales, biomes, and variable sky conditions.
It calculates a layer's output from its net input. The function generates outputs between zero and 1 as the neuron's net input goes from negative to positive infinity. The training of the ANN is completed by a regression analysis being performed internally by the algorithm between the target variable, i. e. the observed and predicted daily shortwave radiation (R Sd_pred and R Sd_pred ). X j = n j =1 w ij y j b j (2) y j = 2 1 + exp -2X j -1 (3) y j = X j (PURELIN) (4) The Bayesian regularisation algorithm was chosen for the optimisation process because it is able to handle noisy datasets by continuously applying adaptive weight minimisation, and can reduce or eliminate the need for lengthy cross-validation that often leads to overtraining and overfitting of models  (Burden and Winkler, 2009).
Datasets Daily and half-hourly data on R S (W m -2 ), R STOA , net radiation (R n , W m -2 ), latent heat flux (λE, W m -2 ), sensible heat flux (H , W m -2 ), and ground heat flux (G, W m -2 ) measured by the FLUXNET  (Baldocchi et al. , 2001)  eddy covariance network were used. A total of 126 sites from the years 1999 to 2006, distributed between latitude 0-90 • north and south of the Equator, were used for the present analysis. The data sites covered a broad spectrum of vegetation functional types and climatic conditions and a list of the sites are given in Table  S1  in the Supplement. Among 126 sites, 85 sites were used for training and the remaining 41 sites were used for validation. Partition of the data into training and validation was randomly selected regardless of the year. These translated into 194 and 86 yearly data for the respective samples.
A global distribution of the data sites is shown in Fig. 3. From the training dataset, three samples were internally generated by the algorithm, i. e. training datasets, validation datasets, and a testing dataset in a percentage ratio of 80 : 15 : 5 respectively. The ANN algorithm is designed to validate its performance for any given training which in most cases should be sufficient for validating the network. However, to ensure the network is robust, we further test the generated network with an independent dataset. Considering the equatorial crossing time of different polar orbiting sensors like LANDSAT, ASTER, and MODIS Terra-Aqua, unique networks were generated for different time of day from morning to afternoon, and thus we had a total of eight networks to represent potential satellite overpass times between 10:30 and 14:00 LT using 30 min intervals as the closest reference time for each hour.
Intercomparison of ET i upscaling methods An intercomparison of three different ET i upscaling methods is performed with the homogeneous datasets to assess their relative performance across a range of temporal scales and variable sky conditions. These are: a. The R S -based upscaling method, where ANN-predicted R Sd is used in conjunction with observed R Si to predict ET d using Eq. (  1 ). b. The exo-atmospheric irradiance method  (Ryu et al. , 2012)  where the reference variable is R S TOA. R Sd TOA = S sc 1 + 0. 033 cos 2π t d 365 cos θ Z , (5) SF RTOA = R Sd TOA R Si TOA , (6) ET d = ET i SF RTOA , (7) where S sc is the solar constant (1360 W m -2 ), t d is the day of the year (DoY), and θ Z is the solar zenith angle. L. Wandera et al. : An Artificial Neural Network approach b. The EF-based method  (Cammalleri et al.
In the new ANN, we used data from those sites where coincident measurements of P and SM were available along with R S and ET, and validated ET d predictions of the new ANN on independent sites. The analysis revealed a 34 % reduction in RMSE (from 3. 28 to 2. 88 MJ m -2 d -1 ), 16 % reduction in MAPE (from 90 to 76 %), and 49 % reduction in mean bias (from 0. 76 to 0. 39 MJ m -2 d -1 ) for persistent cloudy-sky cases (i. e. τ 1 scenarios) from 11:00 LT upscaling. However, no significant improvements in ET d_pred were evident for τ 2 , τ 3 , and τ 4 or for any of the τ classes from the afternoon (13:30 LT) upscaling (Fig. 9 ). ET d is generally controlled by radiation and soil moisture availability. Under the radiation-controlled conditions, ET d is generally not limited due to soil moisture and 70-75 % of the net radiation is contributed to ET d.
, 2014; Kim et al. , 2012). The performance of the method in the semi-arid shrublands appear to be promising (Fig. 11 ), and therefore the method also seems to be credible under water-stressed environments. Given that this analysis was based on FLUXNET sites distributed across 0-90 • latitude north and south, the training datasets cover substantial climatic and vegetation variability. The percentage distribution of the training data according to vegetation type was 23 % crops, 31 % deciduous broadleaf forest, 10 % evergreen broadleaf forest, 20 % evergreen need leaf forest, 8 % grassland, 7 % shrubs and 1 % aquatic, as indicated in Table  S1. The number of grassland and shrubs as indicated were relatively fewer compared to the crops and forest sites. However, biome-specific error statistics (Fig.
11 ) indicated the absence of any systematic errors due to vegetation sampling with the exception of EBF. Availability of more EBF sites in the training datasets is expected to reduce the cloudy-sky errors substantially, due to the assimilation of more cloud information into the R S -based ANN training. The tendency towards positive bias in ET d_pred from both R S and R S TOA in clear skies from afternoon upscaling is partly explained by the fact that, during the afternoon, the values of both R S and R S TOA reached maximum limit and this dominates their daily values  (Jackson et al. , 1983). The post-afternoon rate of reduction in ET does not coincide with the shortwave radiation due to stomatal controls on ET, and the total water flux from morning to afternoon (07:00 to 13:00 LT) is generally greater than the total water flux from post afternoon (15:00 LT onwards) till sunset.
Impact of energy balance closure on ET d_pred FLUXNET EC sites have long been identified as prone to surface energy budget imbalance, which might lead to ±20 % to ±40 % under measurement of latent heat fluxes. In order   4 ) for both "closed" and "unclosed" surface energy balance datasets. These are the subsets of the data where all four SEB components (λE, sensible heat flux, ground heat flux, and net radiation) were available and SEB was closed by the residual SEB closure method  (Foken, 2006). Table  4  revealed substantially low RMSE (10 to 60 %), R 2 (8 to 100 %) and MAPE (1 to 75 %) in ET d_pred when ET i upscaling is done by "unclosed" SEB. A consistently high positive mean bias (0. 63 to 3. 83) in ET d_pred with "closed" SEB was also noted (Table  4 ). Although various methods exist to close the surface energy balance, the impact of various SEB closure methods on ET d_pred statistics is beyond the scope of the current study.
The datasets used for the ANN model development covers a wide range of biome, climate, and variable sky conditions. Therefore, we assume that the R Sd prediction from ANN captures a broad spectrum of radiative forcings, which is also reflected in the independent validation of R Sd and ET d (Figs. 5, 7, Table  2 ). However, the performance of this model for satellite retrieval of R Sd (from R Si ) is dependent on the accuracy of R Si retrieval  (Loew et al. , 2016). Also, the distribution of sites over the tropics, Africa, and South-East Asia is poor, and availability of more sites in these regions is expected to make the ANN model performance more robust. Based on measurements from 126 flux tower sites, we found R S -based upscaled ET d to produce a significant linear relation (R 2 = 0. 65 to 0.
Stomatal control is significantly dependent on the thermal longwave radiative components, and, therefore, the relative proportion of downwelling and upwelling longwave radiation is expected to be a stomatal constraint. However, the availability of longwave radiation measurement stations in the FLUXNET datasets is limited to formulate ANN and evaluate this hypothesis. In general, the stomatal and biophysical constraints are imposed in state-of-the-art thermal remote-sensing-based ET i retrieval schemes, and, therefore the ANN framework can be applied to upscale remotesensing-based ET i to ET d. Also, relatively good performance of the model in semi-arid shrubland further indicated the applicability of the method in water-stressed ecosystems where stomatal controls are predominant. Among all the upscaling methods tested, the R S -based method carries maximum information on the cloudiness and generally produced the lowest RMSE, low bias (Table  3 ), and, therefore, overall the preferably robust scaling mechanism (at the daily scale).
Data availability The FLUXNET eddy covariance datasets are available through http://fluxnet. fluxdata. org (Baldocchi et al. , 2001). The Supplement related to this article is available online at doi:10. 5194/hess-21-197-2017-supplement. Author contributions. Kaniska Mallick designed the analysis, Loise Wandera performed the research, Kaniska Mallick and Loise Wandera developed the manuscript, and all the coauthors jointly contributed to editing the manuscript. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. The authors thank the HiWET (High resolution modelling and monitoring of water and energy transfers in WETland ecosystems) project funded through the Belgian Science Policy (BELSPO) and FNR under the programme STEREOIII (INTER/STEREOIII/13/03/HiWET; CONTRACT NR SR/00/301).
We thank the entire FLUXNET site PIs for sharing the eddy covariance data. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux,.
Microarray analysis was also performed on five of the six samples (the quality of RNA for sample m137 was not sufficient for analysis). Of the 407 mouse orthologs contained in the Sanger cancer consensus genes database (http://cancer. sanger. ac. uk/census/), 14 (3. 4%) displayed expression levels that were altered >2-fold when compared to wildtype thymocytes (Brca2, Card11, Ccnd1, Gnaq, Hmga1, Il21r, Itk, Jazf1, Notch1, Nr4a3, Pdgfrb, Recql4, Rel, and Tfrc). All sequenced samples except m313 (adoptive transfer recipient) showed evidence of substantial copy number decreases on the X chromosome, in comparison to Sca1 + bone marrow progenitors. Also, substantial copy number gains on chromosome 15 were shown to be common for mice receiving the low dose of gc-positive progenitors (m137, m189, m191, and m209).
In the malignant blasts examined (n = 5), elevated levels of these transcripts were observed when compared to normal wild-type thymocytes; c-myc, Dtx1, and Hes1 were found to be upregulated by 6. 85 ± 1. 80, 2. 47 ± 0. 54, and 2. 78 ± 0. 26-fold, respectively (data not shown). Upregulation of Hes1 (9. 78-fold, FDR = 0. 021) and c-myc (3. 25-fold, FDR = 0. 073) in malignant blasts was also detected by microarray analysis.
SNVs were detected using VarScan,  43  InDels were called by SAMtools,  44  CNVs were called by BGI's proprietary method CNV_Detection, and SVs were called by CREST. 45 Copy number ratios <0. 5 were considered as deletions and >1. 5 as amplifications. ANNOVAR software  46  was used to annotate the variant results. The Integrative Genomics Viewer (IGV) 2. 3  47  was used to visually inspect sequence reads. BAM files and associated metadata in XML format have been uploaded to the European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena) under the study identification number PRJEB8006.
Gene Expression Arrays Total RNA for expression analysis was isolated using the RNeasy Mini Kit (QIAGEN), and transcriptome profiling was performed by The Ramaciotti Centre for Gene Function Analysis (University of New South Wales). Briefly, total RNA samples (500 ng/sample) were amplified and labeled according to the manufacturer's instructions (Affymetrix). Amplified RNA was quantified using a NanoDrop (Thermo Scientific) and distribution of the amplified material was analyzed in a 2100 Bioanalyzer (Agilent). Amplified cRNA was hybridized onto Affymetrix mouse 2. 1 gene arrays; the arrays were scanned and expression measurements extracted using the Affymetrix Expression Console v. 1. 3 software. Microarray data have been deposited at ArrayExpress (http://www. ebi. ac. uk/arrayexpress/) under the accession number E-MTAB-3159.
Gene Expression Data Analysis Robust Multi-array Average (RMA) was applied to normalize the expression data,  48  and the remaining analysis was carried out using Bioconductor package  49  in the R statistical language 3. 01. Empirical Bayes statistics for differential expression were calculated using limma 3. 16. 6. 50 p values were adjusted by Benjamini & Hochberg's method to account for multiple test correction. 51 Genes with expression change R2-fold and adjusted p value (FDR) %0. 05 were considered as differentially expressed.
Frameshift mutations all result in the introduction of a premature stop codon and associated loss of the PEST domain; indicated position is relative to GenBank accession AB100603. 1. Mutations were not identified in the HD or TAD domains. b Two break points were identified. c m313 is the secondary recipient of m91 leukemic blasts. www. moleculartherapy. org Molecular Therapy: Nucleic Acids Vol. 6 March 2017.
ACKNOWLEDGMENTS We thank  Professor Stephen Nutt  ( Walter and Eliza Hall Institute, Parkville, Australia ) for providing the gc À/À Rag2 À/À mice and the  BioResources staff of Children's Medical Research Institute  for assistance in maintaining the animal lines used in this study. This work was supported by a project grant ( 632657 ) from the  National Health & Medical Research Council (NHMRC) of Australia , a project grant (grant reference number  11-0563 ) from the  Association  for.
a Frameshift mutations all result in the introduction of a premature stop codon and associated loss of the PEST domain; indicated position is relative to GenBank accession AB100603. 1. Mutations were not identified in the HD or TAD domains. b Two break points were identified. c m313 is the secondary recipient of m91 leukemic blasts. www. moleculartherapy. org Molecular Therapy: Nucleic Acids Vol. 6 March 2017.
The BGV computing farm consists of a server rack with a blade enclosure which is currently filled with three blade servers. The blade enclosure can be filled with up to 16 blade servers to process the data in real time. For commissioning the BGV system, data are analysed offline; after commissioning, the track and vertex reconstruction will be performed on the fly in the BGV CPU farm with the aim to produce a real-time image of the LHC beam and individual bunches.
Summary The usability of the LHCb BGI method for beam imaging was demonstrated during LHC Run1 (2009-2013) and used in 2015 to rapidly obtain a luminosity calibration. These results raised interest to measure transverse beam profiles using beam gas imaging. The BGV demonstrator project was started in October 2012. This system will be able to measure the transverse bunch profile at any LHC beam intensity and throughout all phases of the accelerator cycle, thus giving insight into the beam width and emittance evolution of the beam. The development of the BGV profits from synergies with the SciFi tracker development at LHCb. The BGV detector was recently installed and is now being commissioned. First data from this device will be acquired in the 2016 LHC run.
The signal-to-noise ratio (SNR) time series, the timefrequency representation of the strain data, and the time series data of the three detectors together with the inferred GW waveform, are shown in Fig. 1. The different sensitivities and responses of the three detectors result in the GW producing different values of matched-filter SNR in each detector. Three methods were used to assess the impact of the Virgo instrument on this detection. (a) Using the best fit waveform obtained from analysis of the LIGO detectors' data alone, we find that the probability, in 5000 s of data around the event, of a peak in SNR from Virgo data due to noise and as large as the one observed, within a time window determined by the maximum possible time of flight, is 0. 3%. (b) A search for unmodeled GW transients demonstrates that adding Advanced Virgo improves the false-alarm rate by an order of magnitude over the two-detector network.
The test mass The time series were produced by time shifting the best-match template from the online analysis and computing the integrated SNR at each point in time. The single-detector SNRs in Hanford, Livingston, and Virgo are 7. 3, 13. 7, and 4. 4, respectively. Second row: Timefrequency representation of the strain data around the time of GW170814. Bottom row: Time-domain detector data (in color), and 90% confidence intervals for waveforms reconstructed from a morphology-independent wavelet analysis  [13]  (light gray) and BBH models described in Sec. V (dark gray), whitened by each instrument's noise amplitude spectral density between 20 Hz and 1024 Hz. For this figure the data were also low passed with a 380 Hz cutoff to eliminate out-of-band noise. The whitening emphasizes different frequency bands for each detector, which is why the reconstructed waveform amplitude evolution looks different in each column.
The noise sources that limit LIGO's sensitivity are described in  [24]  and  [25]. For both LIGO and Virgo, commissioning will continue to reach their ultimate designed sensitivities  [26]. Several noise sources that are linearly coupled to the GW data channel can be subtracted in postprocessing, using auxiliary sensors (e. g. , photodiodes monitoring beam motion) and coupling transfer functions calculated via optimal Wiener filters. This technique was used in the initial detector era  [27] [28] [29]. For LIGO, we remove calibration lines, power mains and harmonics, the effect of some length and angular controls, and the effect of laser beam motion. This noise removal can improve the sensitivity of the LIGO detectors by approximately 20%  [30]. For Virgo, we remove the effect of some length controls, and the laser frequency stabilization control.
The difference in significance is due to the different techniques used to rank candidate events and measure the noise background in these searches; however, both report a highly significant event. The significance of GW170814 was confirmed on the full network of three detectors by an independent coherent analysis that targets generic gravitational-wave transients with increasing frequency over time  [55]. This more generic search reports a false-alarm rate < 1 in 5900 years. By comparison, when we limit this analysis to the two LIGO detectors only, the false-alarm rate is approximately 1 in 300 years; the use of the data from Virgo improves significance by more than an order of magnitude. Moreover, this independent approach recovers waveforms and SNRs at the three detectors which are compatible with respect to the coherent analyses used to infer source properties (see Sec.
Incorporating Virgo data also reduces the luminosity distance uncertainty from 570 þ300 -230 Mpc (rapid localization) to 540 þ130 -210 Mpc (full parameter estimation). As with the previous paragraph, the three-dimensional credible volume and number of possible host galaxies also decreases by an order of magnitude  [67] [68] [69] , from 71 × 10 6 Mpc 3 , to 3. 4 × 10 6 Mpc 3 , to 2. 1 × 10 6 Mpc 3. FIG. 3. Localization of GW170814. The rapid localization using data from the two LIGO sites is shown in yellow, with the inclusion of data from Virgo shown in green. The full Bayesian localization is shown in purple. The contours represent the 90% credible regions. The left panel is an orthographic projection and the inset in the center is a gnomonic projection; both are in equatorial coordinates.