dataesr/scientific-paragraphs-categorization huggingface dataset:
Ribonucleic Acid Sequencing Ribonucleic acid was immediately extracted from samples using the RiboPure-Yeast kit (Ambion) and a bead beater. DNasetreated samples were enriched for poly(A) messenger RNA using oligo(dT) beads. Complementary deoxyribonucleic acid libraries were constructed from fragmented samples according to manufacturer's instructions (www. illumina. com) [7]. The RNA-Seq was performed using 1 × 100 base-pair runs on the Illumina MiSeq platform. A 90% cutoff was imposed for minimum similarity and length of reads. Nonspecific matches (reads with multiple hits) were ignored. Reads were mapped to C. albicans coding sequences (SC5314, assembly 19; www. candidagenome. org) using the RNA-Seq module of the CLC Genomics Workbench (www. clcbio. com). Expression values were calculated in RPKM (reads per kilobase exon model per million mapped reads), normalized for total exon-length and number of matches.
Data Analysis Differentially expressed genes were identified using the EdgeR Bioconductor package  [8]. The RPKM was normalized between samples and replicates using the trimmed mean of M-values method  [9]. A gene was considered differentially expressed for false-discovery rate (FDR) and fold change <0. 05 and >4, respectively. Gene ontology enrichment analysis was performed using resources at http://genecodis. cnb. csic. es/ [10] ; hypergeometric P value <. 05 (corrected by FDR) was significant. For comparative purposes, RNA-Seq data from biologic replicates of C. albicans under in vitro (YPD medium, 30°C, 48 hours), ex vivo (OKF6-TERT2 oral epithelial cells), and in vivo (mouse kidneys at 48 hours, and mouse tongues at 5 days) conditions were retrieved from the National Center for Biotechnology Information's Gene Expression Omnibus repository (details in Supplemental Material)  [11, 12].
Across patient and experimental datasets, expression data (>1 RPKM) were available for 5365 genes; 3406 genes were differentially expressed in ≥1 comparison. Hierarchical clustering and principal coordinate analysis revealed that replicates for each condition clustered most tightly (Supplemental Figure  2 ). In vitro and ex vivo replicates clustered more tightly than in vivo replicates. Expression patterns within mouse kidneys and tongues clustered more closely with one another than either did with patient samples. Finally, CDB-D2 was largely dissimilar from D1 samples. Detailed comparisons of C. albicans gene expression in patient and experimental samples are presented in Supplemental Tables  3  and 4. Genes up-regulated in both D1 and D2 biliary fluid compared with in vitro growth were enriched in fatty acid β-oxidation, transmembrane transport (including carbohydrates, glucose, amino acids), thiamine biosynthesis, glyoxylate cycle, hyphal growth, and oxidation reduction.
Twentyone genes that were up-regulated in common between CBD-D2 and in vitro datasets were enriched in ergosterol biosynthesis (n = 4), hyphal formation (n = 5), and oxidation reduction (n = 4). Therefore, stresses imposed by fluconazole's inhibition of ergosterol biosynthesis triggered a range of molecular and cellular responses by C. albicans in vitro and in vivo, including activation of biological processes that in other settings promote pathogenesis. Our results resemble those generated by a nano-String assay for C. albicans transcripts in mouse kidneys after caspofungin exposure, in that the functional spectrum of drug-induced genes was similar to in vitro conditions, but the specific genes induced in vivo were often different  [3]. Taken together, the data indicate that it may be possible to define C.
Acknowledgments Financial support. This work was funded by the  National Institutes of Health  (Grant  1R21AI107290  to M. H. N. ). P. V. is supported by the  National Center for Advancing Translational Sciences  of the  National Institutes of Health  (Award Number  KL2TR000146 ). Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the  ICMJE Form for Disclosure of Potential Conflicts of Interest.
The first measurement with this technique in heavy-ion collisions was performed by the CMS Collaboration, using data from the 2010 Pb-Pb run. The R AA of non-prompt J/ψ in 6. 5 < p T < 30 GeV/c and |y| < 2. 4 was measured to be 0. 37 ± 0. 08(stat. ) ± 0. 02(syst. ) in the 20% most central collisions (see left panel of Figure  54 )  [482]. Preliminary measurements from the larger 2011 dataset explore the p T dependence of R AA  [494]. A recent measurement from the ALICE Collaboration  [480]  (left panel of Figure  54 ) shows a similar value of R AA in a close kinematic range (4. 5 < p T < 10 GeV/c and |y| < 0. 8). Further insights into the parton energy loss can be provided through measurements of reconstructed jets and comparison with theory  [495] , which is complementary to the studies on B hadrons as the reconstructed jet energy is closely related to that of the b quark.
In addition, it is worth noting that the p T of the J/ψ is shifted with respect to the one of the parent B meson (by about 2-3 GeV/c on average in the p T range covered by the CMS measurement), hence the comparison with D mesons is not straight-forward. Preliminary measurements based on higher-statistics data from the 2011 Pb-Pb run at LHC provide a first indication that the nuclear modification factor of B mesons is larger than that of D at transverse momentum of about 10 GeV/c. The measurements were carried out, as a function of collision centrality, for D mesons with 8 < p T < 16 GeV/c and |y| < 0. 5 by the ALICE Collaboration  [497]  and for non-prompt J/ψ mesons with 6. 5 < p T < 30 GeV/c and |y| < 1. 2 by the CMS Collaboration  [494]. With these p T intervals, the average p T values of the probed D and B mesons are both of about 10-11 GeV/c.
The R AA predictions are provided for both RHIC and LHC energies, various light and heavy-flavour probes and different collision centralities. This model does not include any free parameter. All implementation details are provided in Ref. [582]. A representative set of these predictions will be presented in Section 4. 5, while other predictions and detailed comparison with experimental data are provided in Ref. [515, 582, 583]. In summary, this formalism provides a robust agreement with experimental data, across diverse probes, experiments and experimental conditions.
Comparative overview of model features and comparison with data The theoretical models described in the previous sections are compared in Table  11  in terms of their "ingredients" for heavy-quark production, medium modelling, quark-medium interactions, and heavy-quark hadronisation. In this section we compile a comparison of model calculations with heavy-flavour R AA and v 2 measurements by the RHIC and LHC experiments. Figure  66  shows the comparison for D mesons in Pb-Pb collisions at √ s NN = 2. 76 TeV, measured by the ALICE Collaboration  [477, 479]. The left panels show R AA in the centrality class 0-20%, the right panels show v 2 in the centrality class 30-50%. The models that include only collisional energy loss are shown in the upper panels. These models provide in general a good description of v 2.
The TAMU model also includes flow in the hadronic phase. It can be noted that these models predict a similar bump also at LHC energy (left panels of Figure  66 ): the bump reaches R AA > 1 for the models that do not include PDF shadowing, while it stays below R AA = 0. 8 for the models that include it. The present ALICE data for p T > 2 GeV/c do not allow to draw a strong conclusion. However, the preliminary ALICE data reaching down to 1 GeV/c in the centrality class 0-7. 5%  [488]  do not favour models that predict a bump with R AA > 1. The comparisons with measurements of heavy-flavour decay leptons at RHIC and LHC are shown in Figures  68  and 69 , respectively. The R AA and v 2 of heavy-flavour decay electrons in Au-Au collisions at top RHIC energy, measured by PHENIX  [469]  and STAR  [420] , are well described by all model calculations.
The limited size of the pp data sample at √ s = 2. 76 TeV has not allowed ALICE to measure the Υ cross section. The reference adopted by ALICE for the R AA studies  [683]  is, in this case, based on the pp measurement by LHCb  [419]. However, since the LHCb result is obtained in a rapidity range (2 < y < 4. 5) not exactly matching the ALICE one (2. 5 < y < 4), the measurement is corrected through a rapidity interpolation based on a Gaussian shape. For the Υ R AA , CMS results are based on the pp reference cross section extracted from pp data at √ s = 2. 76 TeV  [482]. The number of Υ(1S) with |y| < 2. 4 and 0 < p T < 20 GeV/c is 101 ± 12, with a systematic uncertainty on the signal extraction of ∼ 10%. In Table  16  the datasets and the approach adopted for the evaluation of the pp reference are summarised.
Acknowledgements The SaporeGravis network was supported by the  European Community   Research Infrastructures Integrating Activity "Study of strongly interacting matter" (acronym HadronPhysics3 ) -Grant Agreement no. 283286  -within the  Seventh Framework Programme  ( FP7)  of  EU. The work of T. Dahms was supported by the  DFG cluster of excellence "Origin and Structure of the Universe". The work of R. Rapp was supported by the  US-NSF  grant no. PHY-1306359. The work of E. Ferreiro was supported by the  Ministerio de Economia y Competitividad of Spain. The work of P. B. Gossiaux was supported by the  Region Pays de la Loire  through the  TOGETHER  project. The work of B. Kopeliovich was supported by the  Fondecyt (Chile)  grants  1130543 ,  1130549 ,  1100287 ,and  ECOS-Conicyt  grant No.
C12E04. The work of L. Massacrier was supported by the  European Community   Research Infrastructures Integrating Activity "Study of strongly interacting matter (acronym HadronPhysics3 ) -Grant Agreement no. 283286  -within the  Seventh Framework Programme  ( FP7)  of  EU , and by the  P2IO Excellence Laboratory. The work of  A. Mischke  was supported by the  Vidi grant  from the  Netherlands Organisation for Scientific Research  (project number:  680-47-232 ) and Projectruimte grants from the  Dutch Foundation for Fundamental Research  (project numbers:  10PR2884  and  12PR3083 ). The work of M. Nahrgang was supported by the  Postdoc-Program  of the  German Academic Exchange Service (DAAD)  and the  U. S. Department of Energy  under grant  DE-FG02-05ER41367. The work of B. Trzeciak was supported by the  European social fund  within the framework of realizing the project, Support of inter-sectoral mobility and quality enhancement of research teams at  Czech Technical University in Prague ,  CZ.
Asterisk denote differences at which p < 0. 05 (*p < 0. 05, **p < 0. 01, ***p < 0. 001). et al. 2012). Animals were tested for 70 sessions and and the data was collapsed in 5-session bins for analyses. The dependent variables percent accuracy, correction trials, correct and incorrect response latencies. Fear-conditioning. Impaired contextual and auditory fearconditioning have been observed in a 22q11. 2DS mouse model  (Stark et al. 2008 ). The task is extensively described elsewhere  (Medina et al. 2002). Water maze. The procedure is extensive described elsewhere  (Podhorna and Didriksen 2005). Separate cohorts of 10-week and 25-week old animals were tested with 4 and 6 acquisition days, respectively, followed by a probe trial 24 h later. Data were analyzed using Ethovision 3. 0 (Noldus, Wageningen, The Netherlands).
Statistical Analyses Data was analyzed through one-way ANOVA with genotype as between-subjects variable or repeated-measures ANOVA with genotype (and sex, where appropriate) as between-subject variable(s) and session, delay, stimulus duration, learning phase or stimulus separation as the within-subjects variable. Significant interactions where followed by one-way ANOVA. See Result for experiment-specific information. Analyses were done using SPSS (v21. 0, IBM Corp, Armonk, NY).
Secondly, the residual stress field alone insufficient for calculation of the J -integral; distributions of total strain and strain energy density, including energy dissipated as plastic work for real elastic-plastic materials, are also needed. In this study we propose a combined experimental/ analytical treatment of elastic-plastic fracture in the presence of residual stress. The residual stress and elastic strain fields which exist in a specimen before and after the introduction of a defect are reconstructed from point-wise data measured using neutron diffraction. Using this information along with modelling of the residual stress introduction and fracture loading of the specimen, elastic and elastic-plastic fracture parameters are evaluated explicitly. In this way, the effect of an experimentally-measured residual stress field on elasticplastic fracture can be analysed without resorting to simplified methods such as the R6 V factor to account for the interaction between residual and applied loading.
6 x 0. 6 x 2 mm. At these points, the inter-planar spacing of the {311} plane was measured in the x and z directions, while the spacing of the {222} plane was measured for the y direction. Due to the crystallographic texture of the specimen material it was necessary to use different planes in these different directions to achieve acceptable counting times with this smaller gauge volume. All of these measurements were compared with lattice parameter measurements taken from nominally un-stressed reference specimens in the same orientation, in order to calculate residual elastic strain. For the set of measurements using a gauge volume of 0. 6 x 0. 6 x 2 mm (in which residual elastic strains were measured in three orthogonal directions) residual stresses were calculated from the residual elastic strain data using hkl-specific elastic constants derived using the Kröner polycrystal modelling scheme  [15, 16].
Finite Element Simulation Overview Elastic-plastic finite element analysis of the indented and non-indented specimens was performed. For the nonindented specimen (Model 1 in Fig. 3 ) it was only necessary to model the three-point bend loading, whereas for the indented specimen it was necessary to model the indentation process, cutting of the notch, and three-point bend loading in sequence (Model 2a). In addition to this, the indented specimen was analysed using a combined experimental/modelling approach (Model 2b). Residual elastic strain data measured using neutron diffraction was used to reconstruct the complete stress field in unnotched specimen. This was combined with material hardening state information taken from modelling of the indentation process to generate a model of the indented specimen, from which the notch cutting and three-point bend loading steps were then simulated.
Combined Experimental/Modelling Approach To avoid some of the difficulties involved in accurately simulating the introduction of residual stress, a technique for including measured residual stress data in the simulations was used in Model 2b (see Fig. 3 ). An estimate of the complete residual stress state in the un-notched specimens was reconstructed from measured residual elastic strain data using the iterative technique described previously by Coules et al. [24]  and others  [25, 26]. First, three orthogonal components of the stress tensor at the neutron diffraction measurement locations were calculated assuming a state of plane stress. These were interpolated linearly in the x-y plane over the region in which measurements were taken (y ≤ 10 mm). The three components of the interpolated stress field in the measurement region were applied as an initial condition to a finite element model of the specimen.
14(b)  & (c) ). Overall, the Model 2b approach of measurement, residual stress field reconstruction and then modelling of notch introduction/loading gave better agreement with experimental strain data than the Model 2a method (i. e. modelling the indentation process). The J -integral at fracture was evaluated from the results of each of the three models using fracture loads determined experimentally. Good path-independence of J was observed beyond 1 mm radius from the notch tip (see Fig. 15(a) ) and results evaluated using a circular domain of radius 3 mm were taken to be reliable. The J -integral at fracture is shown in Fig. 15(b ) as a function of the through-thickness dimension z. Assuming that the J -integral is a reliable criterion for fracture initiation in these specimens then the maximum value of J at fracture should be the same for specimens with and without indentation.
The Young's modulus was determined to be 69. 8 GPa and true stress vs. logarithmic plastic strain data for the material are given in Table  2. The Poisson's ratio of the material was not evaluated experimentally but was assumed to be equal to 0. 33 in the subsequent modelling work. Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
2008) , which might also affect the metal accumulation. However, metal toxicity and accumulation are not only influenced by the free metal ions but also by the exposure time as shown with the unicellular algae Pseudokirchneriella subacapitata exposed to cadmium  (Paquet et al. 2015). Moreover, these defense mechanisms, even if L. spicata shows a great capacity to produce high quantity exudates, which able complexing copper for at least 48 h in a constant way (conditional stability constant (log K′) = 8. 6 ± 0. 3)  (Andrade et al. 2010) , the ability for kelp to face a permanent contamination by copper should be explored. Fig. 2  The effect of copper on Zoea I of Taliepus dentatus. Evolution of the mortality was registered every 12 h at all the used concentration of copper and tested by two-way ANOVA completed by a Bonferroni posttest The data revealed that spores of both considered species, in the presence of kelps exudates, might germinate and continue with its development in an environment with moderately high levels of dissolved copper.
Acknowledgements The authors want to thank  Professor Myriam Fernandez Professor Sergio A. Navarrete  for giving us the possibility to do the experiments at the ECIM marine station. Assistance provided by  Veronica Flores  and  Bryan Bularz  is also deeply appreciated. We deeply thank two anonymous reviewers who made significant contributions to improve the first version of this paper. This study was part of the  FONDECYT research program  no. 11060309  ( CONICYT ) to SA. Additional support was provided by  FONDAP   1501-0001  (CONICYT) and by  BASAL FB-0002-2014  to the  Center of Applied Ecology and Sustainability (CAPES). Compliance with ethical standards.
Statistical Analysis All statistical analyzes were performed using the R software (v. 3. 1. 2, R Development Core Team). Mean values are given with their standard error. Plant and soil parameters, as well as AMF colonization, were compared among treatments by using a non-parametric Kruskal-Wallis one-way analysis of variance followed by a Conover post-hoc test whenever significant (PMCMR package,  [22] ).
Patient samples Of 232 HER2 + samples, quality gene expression could be produced from 202/209 (96. 7%) potential samples. All samples were re-evaluated to ensure tumor was present in the specimen. RNA was extracted from formalin-fixed, paraffin-embedded (FFPE) primary breast tumor tissue  (3, 4). Gene expression was performed using Affymetrix U219 GeneChips™ as per Affymetrix protocols on 96 well plates. Quantile RMA normalization  (5)  was performed and adjustment for potential batch effects was done using COMBAT  (6)  with R package SVA  (7)  in R version 3. 1. 1, available at Bioconductor 2. 12 (www. bioconductor. org). If multiple probe-sets were available for the pre-selected immune genes, the one with the highest variance in all samples profiled was used. Gene expression data for this cohort are available from Gene Expression Omnibus (GEO) database accession no: GSE65095.
A. Detector and Readout Data taken with four Ge detectors are adopted in this study. The detectors and their characteristics are listed as follows, while their respective sensor electrode schematics are depicted in Figures  3a, b ,c&d. 1. Conventional coaxial p-type high purity Ge detector (CoaxGe) with 1 kg in mass, used as target detector in Ref. [7] ; 2. 4-element array of n-type Ge detector with 5 g modular mass, a pilot "ultra-low-energy" germanium detector (ULEGe) used in Refs. [19, 22] ; 3. p-type point-contact Ge detector (pPCGe) with 500 g mass, similar in functionalities but not in detector mass to those used as target detectors in Refs. [20, 23] ; and 4. n-type point-contact Ge detector (nPCGe) with 500 g mass, used as calibration detector in Refs. [3, 20]. All the detectors have been procured commercially.
As depicted in Figure  4a , the preamplifier signals are further processed by both shaping and timing amplifiers  5. Output from the timing amplifier (TA) preserves rise-time information for distinguishing bulk and surface events (Section IV C 2). The shaping amplifier signals at 6 µs shaping time (SA 6 ) are optimized for energy measurement. The discriminator output provides the trigger instant for data acquisition (DAQ). Pulses from TA and SA 6 are digitized by 200 and 60 MHz flash analog-todigital converters 6  , respectively. A typical SA 6 pulse at 4. 2 keV ee for pPCGe is shown in Figure  4c.
Data from pPCGe are used as illustrations. The selected events except random-trigger ones are of effective energy near noise-edge (∼ 300 eVee in this example). Their amplitude is normalized to unity in the display, except for random-trigger events whose normalization follows that of self-trigger events. The physics samples are from bulk events tagged with "AC + ⊗CR + " and after basic filters of Section IV C 1. The trigger instants defined by Ge-SA6 signals are shown. The physics and test-pulser events are identical in SA6 but differ in TA. The physics and self-trigger noise events show different profiles in both amplifiers and are therefore, in principle, distinguishable. work are summarized in Table  I.
Therefore, external γ's with energy less than 50 keV are totally suppressed and cannot be used. Instead, calibration is provided  [20, 21, 23]  by internal cosmogenic radioactivity, the strongest of which are peaks from electron capture of 68,71 Ge producing Ga X-rays (10. 37 keV and 1. 29 keV). This calibration scheme also applies to nPCGe which does not have a thin entrance window and is housed in a copper cryostat of 1 mm thickness. In all the detectors studied, no physics-related structures are observed below ∼ 1 keV ee , where energy calibration is performed using the test-pulser. Data were taken with decreasing pulser amplitude. Pulser measurements at zero-amplitude are equivalent to randomtrigger events. The energy scale of the pulser is defined by matching to the γ-peaks at high energy.
D. Quenching Factor Quenching factor (QF) is the ratio of ionization energy to the nuclear recoil energy deposited by radiations. In Ge detectors, ionization energy corresponds to the amount of electron-hole pairs created. Knowledge of QF is essential in studies on νN and χN processes in Ge ionization detectors, the signatures of which are due to nuclear recoils. A compilation of existing QF measurements in Ge  [4]  is given in Figure  11. Superimposed are calculations obtained using the TRIM software  [29]  and the Lindhard model  [30]  at two popular parametrizations (k=0. 157 and 0. 20). Both schemes have been used in various dark matter experiments. The TRIM results explain the QF data at a larger energy range and were adopted in our previous analysis with an assigned systematic uncertainty of 10%  [19, 20, 22, 23].