dataesr/scientific-paragraphs-categorization huggingface dataset:
The efficiency was measured using electrons in the region of E e > 18 GeV and checked at low E e using a sample of QED Compton events. It is found to be 96 % in data with a difference of 1 % between data and simulation. This difference was corrected for and a 1 % uncorrelated uncertainty is adopted. Table  4  The NC e + p reduced cross section σNC (x, Q 2 ) for E p = 575 GeV and P e = 0 with total (δ tot ), statistical (δ stat ), total uncorrelated systematic (δ unc ) errors and two of its contributions from the electron energy error (δ E unc ) and the hadronic energy error (δ h unc ). The effect of the other uncorrelated systematic errors is included in δ unc. In addition the correlated systematic (δ cor ) and its contributions from a positive variation of one standard deviation of the electron energy error (δ E + cor ), of the polar electron angle error (δ θ + cor ), of the hadronic energy error (δ h + cor ), of the error due to noise subtraction (δ N + cor ), of the error due to background subtraction charge asymmetry (δ S + cor ) and of the error due to variation of the cut value on the electron discriminator D ele (δ D + cor ) are given.
Electron identification: A calorimetric algorithm based on longitudinal and transverse shower shape quantities is used to identify electrons in the E p = 460 and 575 GeV data sample. The efficiency of this selection can be estimated using a simple track based electron finder which searches for an isolated high p T track associated to an electromagnetic energy deposition. The efficiency is well described by the simulation and the uncertainty of 0. 2 % (0. 5 %) is assigned in the nominal (high y) analysis at z imp < 20 cm. For z imp > 20 cm the uncertainty is taken to be 1 % due to the lack of statistics in this region selected by the track based algorithm.
Double differential cross sections The reduced cross sections σNC (x, Q 2 ) for P e = 0 are measured in the kinematic range 35 ≤ Q 2 ≤ 800 GeV 2 and 0. 00065 ≤ x ≤ 0. 65 at two different centre-of-mass energies and are referred to as the LAr data. The data are presented in  Tables 3, 4  and shown in Fig. 4. The figure also includes previously published H1 data  [3]  in the Q 2 range of the new data reported here, and are referred to as the SpaCal data. The published 920 GeV e + p LAr data  [5]  are scaled by a normalisation factor of 1. 018  [33]. This correction factor arises from an error in the Compton generator used in the determination of the integrated luminosity of the HERA-II 920 GeV data set. The new LAr data provide additional low x measurements for Q 2 ≥ 35 GeV 2 (from the E p = 460 and 575 GeV data sets).
There is no correlation between LAr and SpaCal measurements except for a common integrated luminosity normalisation of the LAr and SpaCal data at E p = 460 and 575 GeV. For low y ≤ 0. 35, Fig. 4  The reduced cross section σNC (x, Q 2 ) + 0. 3i measured at three proton beam energies E p = 460 GeV (diamonds, i = 0), 575 GeV (squares, i = 1) and 920 GeV (circles, i = 2). The previously published H1 SpaCal data are shown by the open symbols. The solid symbols are the H1 LAr data. The new measurements reported here correspond to the filled diamonds and squares. The inner error bars represent the statistical errors, the full error bars include the statistical and systematic uncertainties added in quadrature, excluding the normalisation uncertainty. The curves represent the prediction from the H1PDF2012 NLO QCD fit 2 = 35 GeV 2 Q 2 = 45 GeV 2 Q 2 = 60 GeV 2 Q 2 = 90 GeV 2 Q 2 = 120 GeV 2 Q 2 = 150 GeV 2 Q 2 = 200 GeV 2 Q 2 = 250 GeV 2 Q 2 = 300 GeV 2 Q 2 = 400 GeV 2 Q 2 = 500 GeV 2 Q 2 = 650 GeV 2 Q 2 = 800 GeV 2 Q = 460 GeV (i=0) p E = 575 GeV (i=1) p E = 920 GeV (i=2) p E p, LAr Fig.
5 ≤ Q 2 ≤ 45 GeV 2  [3]. The values of R as a function of Q 2 are determined by minimising the χ 2 function of Eq. 6 in which F L is replaced by R = σ L σ T = F L F 2 -F L. (7) F L = R 1 + R F 2 assuming the value of R is constant as a function of x for a given Q 2. The minimum is found numerically in this case, using the MINUIT package  [73]. The asymmetric uncertainties are determined using a MC method in which the mean squared deviation from the measured value of R is used to define the asymmetric uncertainties. The resulting value of R(Q 2 ) is shown in Fig. 9. The measurements are compared to the prediction of the HERAPDF1. 5 NNLO for √ s = 225 GeV and y = 0. 7. The expected small variation of R in the region of x in which the data are sensitive to this quantity is also shown. The data are found to be consistent with a constant value across the entire Q 2 range shown.
Acknowledgments We are grateful to the  HERA machine group  whose outstanding efforts have made this experiment possible. We thank the engineers and technicians for their work in constructing and maintaining the H1 detector, our funding agencies for financial support, the  DESY  technical staff for continual assistance and the  DESY  directorate for support and for the hospitality which they extend to the non DESY members of the collaboration. We would like to give credit to all partners contributing to the  EGI computing infrastructure  for their support for the H1 Collaboration. We would also like to thank the members of the MSTW, CT, ABM, JR, NNPDF and HERAPDF collaborations for their help in producing theoretical predictions of F L shown in Fig. 8. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Slides were scanned immediately after washing on the Affymetrix GeneChipH Scanner 7G Plus, according to the manufacturer's protocol. The Bovine Genome Array contains 24128 probe sets which represent 15264 UniGenes (annotation from May 2006) to measure the global transcripts. The Bovine Genome Array annotation available from NetAffx TM Analysis Center (Bovine. na29. annot. csv) was used. The microarray data has been deposited in the Gene Expression Omnibus database (accession number: GSE50685).
Analysis of Microarray Data The raw data was analyzed using Bioconductor software  [24]  in RStudio  [25]. Normalization of expression values was performed using the Robust Multiarray Average (RMA) algorithm as implemented in the Affy package. Differential expression of each gene was assessed using empirical Bayes models which were implemented in the R package Limma  [26]. Briefly, the model allowed for testing differences between treatment groups using a moderated Student's t-test statistic to identify significantly differently expressed genes. This test moderates residuals of the standard deviations across the probe sets to obtain more stable interferences for each transcript. Probes were considered to be differentially expressed (DE) if the false discovery rate adjusted Pvalue was below 0.
05 and had a minimum fold change of 2. Similarity between samples was determined with the Gene Distance Matrix function in Multi-experiment Viewer (MeV version 4. 8. 01) [27]  using Pearson correlation as a distance metric. Annotation and pathway analysis was performed using the online software tool DAVID (Database for Annotation, Visualization and Integrated Discovery)  [28]. Overrepresentation of gene sets defined by the group of biological process (BP) in the Gene Ontology (GO) data base or the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were tested using Fisher's exact test. Only significant DE genes which were annotated with an Entrez gene ID were included.
Real Time PCR Analysis Quantitative real time PCR (qPCR) was performed with the same RNA samples as used for the microarray hybridization. Synthesis of cDNA was performed with an amount of 1 mg total RNA using SuperScript II Reverse Transcriptase and oligo (dT) nucleotide primers (both Invitrogen, Life Technologies, Carlsbad, CA) according to the manufacturer's instructions. Thermocycling was performed in a StepOnePlus Real Time PCR system with SYBR Green Master Mix (both Applied Biosystems, San Francisco, CA) using the following conditions: 95uC for 10 min, followed by 40 cycles of 95uC for 15 sec and 60uC for 60 sec with fluorescence detection during the annealing/extension step. The quality of amplification was verified by melt curve analysis. Primer sequences, references, accession numbers and concentrations used for amplification reactions are indicated in table  1.
Analysis of qPCR Data Two genes with known stable expression in udder tissue which were also not present on the list of differentially expressed genes from the microarray experiment, Ubiquitously expressed transcript (UXT) and Mitochondrial Ribosomal Protein L39 (MRLP39), were used as reference genes  [29]. The cycle number required to achieve a definite SYBR Green fluorescence signal was calculated by the StepOne Software v2. 3 (Applied Biosystems, San Francisco, CA) as cycle threshold (C t. ) and is inversely correlated to the amount of target gene in the sample. The C t of the reference genes showed no significant difference between all samples and a reference index was calculated to normalize the C t of the target genes. The relative quantity (RQ) of the normalized target gene in samples of infected and treated quarters compared to the uninfected and untreated control quarters was calculated by the DDC t method in StepOne Software v2.
Validation of Selected Genes by Real Time qPCR The microarray results were validated by qPCR of the genes that were differently expressed in treated quarters at the respective time points representing the significantly regulated pathways. For the chemokine signaling pathway, CCL2, CXCL2 and CXCL8 were analyzed. For the TLR-signaling pathway, LBP and IL-12A were measured. Figure  3  shows relative expression values compared to NC quarters for the two sampling time points. Relative expression appeared to be lowest in CFPD treated quarters for all tested genes. For CCL2 and CXCL2 this is in line with the microarray result (Fig. 3 A ). Interleukin-12A qPCR data at 24 h post challenge is in conflict with the microarray result (Fig. 3 A ). However, none of the differences were statistically significant for the 24 h time point.
Patients The inclusion criteria comprised age between 0-18 years and a diagnosis of de novo AML or myelodysplastic syndrome (MDS) with t(6;9)/DEK-NUP214 made between 1 January, 1993 and 31 December, 2011. The study was conducted within the International Berlin-Frankfurt-Munster study group cooperation and 24 study groups and treatment centers participated, providing background data for frequency analysis and submitting clinical and genetic data on 62 patients. In addition, the study groups contributed smears, bone marrow biopsies, and material for gene expression analysis. The nomenclature was reviewed following the International System for Human Cytogenetic Nomenclature 2009  19  by three of the coauthors (JH, BJ, and EF). Available diagnostic smears and bone marrow biopsies were reviewed by co-author GK.
Gene expression profiling and quantitative real time polymerase chain reaction analysis Gene expression profiling was performed on 297 pediatric AML patients' samples of which eight were t(6;9)-positive. The gene expression profiling data on the full cohort have already been published but an individual analysis of t(6;9)/DEK-NUP214 was not performed. 20 Original data are available in the Gene Expression Omnibus repository (http://www. ncbi. nlm. nih. gov/geo; accession GSE17855). The gene expression profiles of t(6;9)-positive cases were compared with those of other representative pediatric AML samples. 20 Four top scoring, differentially expressed genes were selected for mRNA expression validation by quantitative real time polymerase chain reaction (RT-qPCR) analysis based on statistical significance, occurrence of multiple probes in the top-list, and logfold change, combined with potential biological relevance; EYA3 (eyes absent homolog 3, Drosophila), SESN1 (sestrin 1), PRDM2/RIZ (PR domain containing 2, with ZNF domain), and HIST2H4 (histone cluster 2, H4).
Statistical analyses The Kaplan-Meier method was used to estimate the 5-year probabilities of overall survival and event-free survival. The 5-year cumulative incidence of relapse was calculated by the method of Kalbfleisch and Prentice. 21 The median time to HSCT in first complete remission was 150 days, with 98 days as minimum. Patients with an event within 150 days of treatment were excluded from the analysis comparing the effect of HSCT in first complete remission with that of conventional chemotherapy alone. Statistical analyses were conducted using SPSS for Mac, version 20 (SPSS Science, Chicago, IL, USA). Gene expression profiling data were acquired using Expresso (Bioconductor package Affy). Probe-set intensities were normalized using variance stabilization normalization (Bioconductor package VSN) in the statistical data analysis environment R, version 2.
Morphology The majority of t(6;9) cases were categorized as FAB type M2 (54%) or FAB type M4 (26%) (Table  1 ). Peripheral blood and bone marrow smears from 11 and 15 cases, respectively, of which two were MDS cases, were evaluable for review along with 7 bone marrow biopsies. Dysplasia was defined according to EWOG-MDS guidelines. 25 All bone marrow biopsies displayed mild to moderate bilinear dysplasia. Basophils were present in the bone marrow from five (33%) of the reviewed cases, but did not exceed 2% in any case. Data on basophils were available for 16 cases without central review; five had 0. 5-2% basophils in the bone marrow while no basophils were reported in the remaining 11 patients. No Auer rods were identified in the material reviewed. Among the cases not centrally reviewed, Auer rods were reported in ten of 28 (36%) cases.
Acknowledgments The authors would like to thank all groups involved in retrieving and providing data and material (a list of additional participants involved in the study is given in the online supplement). We especially thank  Saint-Louis Hospital's Tumor Biobank in Paris (France) , the  NOPHO Biobank in Uppsala (Sweden) , the  St. Jude Children's Research Hospital Tissue Resources Laboratory in Memphis (USA) , and the  Giuseppe Basso Laboratory of Pediatric Oncology, Pediatric Department, University of Padova, Padova (Italy)  for managing patients' samples, and for valuable samples and smears provided by regional treatment centers. We thank  Susan Arentsen-Peters  at the  Laboratory of Pediatric Oncology/Hematology, Erasmus MC, Rotterdam, NL  for technical assistance. This work was supported by the  Danish Childhood Cancer Foundation , the  Swedish Childhood Cancer Foundation , the  Dutch Childhood Oncology Group , a Grant from the  Clinical Cancer Research  and  Grant-in-Aid for Cancer Research  from the  Ministry of Health, Labour and Welfare of Japan.
2 ) was recorded at 8. 9 m w. e. depth with a larger mean mass equivalent diameter of 10 µm. This event could be associated with the major dust outbreak over south Asia in June 2003 described by  (Gautam et al. , 2013). The process by which dust is deposited onto snow at Mera Glacier is uncertain, but concentrations are clearly higher than would be expected according to atmospheric measurements made at NCO-P. As dust deposition fluxes were high in all three seasons, we used the available data to track changes in the sources of dust. Soluble species analyzed by ion chromatography, including calcium, magnesium, manganese, lithium and strontium, were used to calculate the ratios of soluble ionic species to dust. The negative ionic balance was attributed to the carbonate load. These ratios and carbonate were used to identify changes in the sources of dust.
To evaluate this impact on Mera Glacier, we compared these values with the mass-balance and energy-balance measurements made on the glacier since 2007 and 2009, respectively. Using meteorological data collected on Naulek glacier by the automatic weather station (AWS) at 5360 m a. s. l. (Fig. 1 ), the surface energy balance was calculated at point scale to derive local melting and sublimation from November 2009 to October 2010, following the method of  Wagnon et al. (2003). The four components of the radiative balance were measured directly in the field using a CNR1 Kipp & Zonen sensor. The sensible and latent heat fluxes were com- puted with the bulk aerodynamic approach including stability correction, using one level of measurements of air temperature, relative humidity and wind speed. During the melting season, the other terms of the surface energy balance (heat supplied by precipitation, and conductive heat flux inside the glacier) are negligible compared to the other fluxes.
Acknowledgements. We would like to thank  K. Fujita , an anonymous reviewer and interactive comments for providing useful comments and suggestions for the initial version of the manuscript. This work was supported by the  French Service d'Observation GLACIOCLIM , the  French National Research Agency  through  ANR-09-CEP-005-01/PAPRIKA , and internal LGGE funding. We thank the porters who were involved in field trips and coring operation, the  Ev-K2-CNR  and their Nepalese technicians for the assistance at the  NCO-P. Edited by:  T. Bolch The publication of this article is financed by  CNRS-INSU.
8 (Roche, 454 Life Sciences). Contigs obtained were combined together by Opera software v1. 2 (6) combined with GapFiller v1. 10 (7) to reduce the set. Finally, some manual refinements using CLC Genomics v5 software (CLC bio, Aarhus, Denmark) and homemade tools (http://bioinfomed. fr) improved the genome. The draft genome of A. felis consists of 11 scaffolds of 322 contigs containing 3,936,701 bp and an estimated size including gaps of 3,989,646 bp. The GϩC content of this genome is 61. 27%, which is similar to other closed A. felis species (60. 7%). Noncoding genes and miscellaneous features were predicted using RNAmmer  (8) , ARAGORN  (9) , Rfam  (10) , PFAM  (11). Open reading frames (ORFs) were predicted using Prodigal  (12)  and functional annotation was achieved using BLASTP against the GenBank database (13) and the Clusters of Orthologous Groups (COGs) database  (14, 15).
The genome was shown to encode at least 53 RNA predicted genes including 3 rRNAs in a single operon and 50 tRNAs. 4,140 genes were also identified, representing a coding capacity of 3,369,783 bp (85. 59% of the sequenced genome). Among these genes, 113 (2. 72%) were founded as putative protein and 2,163 (52. 24%) were assigned as hypothetical proteins. Moreover, 2,929 genes matched a least one sequence in the COGs database with BLASTP default parameters. Further, A. felis presents 25 genes annotated as resistance protein such as those related to copper resistance. Nucleotide sequence accession numbers. The A. felis 76713 genome has been deposited at EMBL/EBI under the accession numbers CCAZ020000001 to CCAZ020000011.
Our framework is generally applicable for interpreting 3D point cloud data acquired via airborne laser scanning (ALS), terrestrial laser scanning (TLS), mobile laser scanning (MLS), range imaging by 3D cameras or 3D reconstruction from images. While the selected feature subset may vary with respect to different datasets, the beneficial impact of both optimal neighborhood size selection and feature selection remains. Further extensions of the framework by involving additional features such as color/intensity or full-waveform features can easily be taken into account. The paper is organized as follows. In Section 2, we explain the single components of our framework in detail. Subsequently, in Section 3, we evaluate the proposed methodology on MLS data acquired within an urban environment. The derived results are discussed in Section 4.
The values rmin and rmax depend on various characteristics of the given data and are therefore specific for each dataset. However, the results are based on the assumption of particular shapes being present in the observed scene. In order to avoid assumptions on the scene, we propose a more general solution to optimal neighborhood size selection. Since the eigenvalues correspond to the principal components, they span a 3D covariance ellipsoid. Consequently, we may normalize the three eigenvalues by their sum Σ λ and consider the measure of eigenentropy E λ given by the Shannon entropy according to E λ = -e1ln(e1) -e2ln(e2) -e3ln(e3) (4) where the ei with ei = λi/Σ λ for i ∈ {1, 2, 3} represent the normalized eigenvalues summing up to 1. The eigenentropy thus provides a measure of the order/disorder of 3D points within the covariance ellipsoid 2.
Datasets For our experiments, we use the Oakland 3D Point Cloud Dataset 3   (Munoz et al. , 2009a)  which is a labeled benchmark MLS dataset representing an urban environment. The dataset has been acquired with a mobile platform equipped with side looking SICK LMS laser scanners used in push-broom mode. A separation into training set X , validation set V and test set Y is provided, and each 3D point is assigned one of the five semantic labels wire, pole/trunk, facade, ground and vegetation. After class rebalancing, the reduced training set encapsulates 1,000 training examples per class. The test set contains 1. 3 million 3D points. Additionally, we apply our framework on the Paris-rue-Madame database 4   (Serna et al. , 2014)  acquired in the city of Paris, France. The point cloud data consists of 20 million 3D points and corresponds to a street section with a length of approximately 160 m.
For data acquisition, the Mobile Laser Scanning (MLS) system L3D2  (Goulette et al. , 2006)  equipped with a Velodyne HDL32 was used, and annotation has been conducted in a manually assisted way. Since the annotation includes both point labels and segmented objects, the database contains 642 objects which are in turn categorized in 26 classes. We exploit the point labels of the six dominant semantic classes facade, ground, cars, motorcycles, traffic signs and pedestrians. All 3D points belonging to the remaining classes are removed since the number of samples per class is less than 0. 05% of the complete dataset. For class rebalancing, we take into account that the smallest of the selected classes comprises little more than 10,000 points. In order to provide a higher ratio between training and testing samples across all classes, we randomly select a training set X with 1,000 training examples per class, and the remaining data is used as test set Y.
, 2013) , • the feature subset SCFS derived via Correlation-based Feature Selection, and • the feature subset SFCBF derived via the Fast Correlation-Based Filter. The latter three feature subsets are based on either explicitly or implicitly assessing feature relevance. In case of combining feature subsets with RF-based classification, the tree depth of the Random Forest is determined as max{ √ d , 3}, since at least 3 features are required for separating 5 or 6 classes. Note that the full feature set only has to be calculated and stored for the training data, whereas a smaller feature subset automatically selected during the training phase has to be calculated for the test data. All implementation and processing was done in Matlab. In the following, the main focus is put on the impact of both optimal neighborhood size selection and feature selection on the classification results.
First, we test our framework on the Oakland 3D Point Cloud Dataset. Since the upper boundary k = 100 has been selected for reasons of computational costs, we have to take into account that it is likely to also represent 3D points which might favor a higher value. Accordingly, we consider the percentage of 3D points which are assigned neighborhoods with k < 100 neighbors which is 98. 12% and 98. 08% for Nopt,dim and N opt,λ. For QDA-based classification based on all 21 features, the derived recall and precision values for different neighborhood definitions are provided in Table  2  and Table 3 , and the respective F1-scores are visualized in Figure  3. The recall and precision values when using a RF classifier are provided in Table  4  and Table 5 , and the respective F1-scores are visualized in Figure  4.