dataesr/scientific-paragraphs-categorization huggingface dataset:
9) and associated regulatory proteins such as inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta, IKBKB (61. 3), IKBKE (64. 4), IKBKG (61. 1) and B-cell CLL/lymphoma 3, BCL3 (62. 5) following TNF-a treatment. These data reinforce the putative major role of NF-kB in the regulation of chemokine expression under these conditions. To confirm the involvement of NF-kB in the regulation of chemokine expression in vivo, we took advantage of a murine model in which the p65 gene, encoding the transcriptional activity of NF-kB, has been overexpressed (Figure  3 ). The expression of all chemokines assessed by qPCR was found to be increased in the white adipose tissue of aP2-p65 mice by at least 9. 5-fold, with Ccl5 and Ccl19 being increased by around 40-fold. Ccl20 mRNA was detected in the adipose tissue of aP2-p65 mice only, with a mean C T value of 29.
This observation suggests that there might be an alternative signaling pathway mediating TNF-a induced chemokine expression. Finally, to investigate the possible involvement of other signaling pathways in TNF-a induced chemokine expression in adipocytes, we preincubated cells with chemical inhibitors of p38, JNK or NF-kB before performing TNF-a stimulation (15 ng/ml). The expression level of CCL2, CCL5 and CXCL10 was then measured using qPCR. While p38 chemical blockade did not modify chemokine expression in response to TNF-a (data not shown), JNK inhibition led to a decrease in CCL5 and CXCL10 expression, whereas CCL2 level was not affected (Figure  5 ). Finally, inhibition of the NF-kB pathway totally suppressed chemokine expression in response to TNF-a. Altogether, these results clearly indicate that NF-kB is the central regulator of chemokine expression in response to TNF-a.
[10]  and Huber et al  [32]. However, it should be noted that the most upregulated genes in response to TNF-a in vitro (i. e. CCL5, CCL19 and CCL20) are not the ones that are overexpressed in obese vs. lean subjects. Discrepancies between the results observed in cell culture and adipose tissue biopsies could arise from 1) methodological aspects: in cell culture only adipocytes were present whereas biopsies also contain other cell types in which gene regulation might be different to what is occurring in adipocytes and therefore could dilute or mask gene regulation in the adipocyte fraction. Furthermore, gene expression results were generated using microarrays in the case of adipocytes whereas qPCR was used to compare chemokine expression between obese and lean adipose tissue, which makes the comparison of data in terms of fold change impossible.
Patients with stable or progressive disease after IC were treated with surgery, usually followed by RT. RT, at a total dose of 70 Gy, was administered in 35 fractions of 2 Gy each, over a 7-week period. CRT consisted of RT plus three cycles of cisplatin at the same doses. In addition, we analyzed the RAB25 expression in HNSCC samples using previously described microarray datasets available in GEO (GEO25099, GSE23558, and GSE26549)  [27] [28] [29] , Array Express (E-TABM-302)  [30]  and MIAME-VICE (Thurlow et al. )  [31]  public databases.
Cells that migrated and were attached to the underside of the filter were stained with crystal violet (Sigma-Aldrich Quimica SA, Madrid, Spain). For invasion assays, we followed the same procedure but the transwell membrane was previously precoated with Matrigel (BD Biosciences, Franklin Lakes, NJ). Transwell experiments were performed three times by duplicate. We took two 1009 magnified images of each transwell membrane under the same lighting and time exposure conditions. Images were quantified using the Metamoph v5. 0 sofware (Universal Imaging, Downingtown, PA), applying the HSI (HUE-saturation-intensity) model as previously described  [32]. Using the set color threshold tool of Metamorph software, we established a 174-255 HUE range to select and quantify the area occupied by crystall violet-stained cells.
Thermal cycling conditions were 1 min at 94°C and 45 cycles at 94°C for 30 sec, at 55°C for 30 sec and 68°C for 1 min. PCR product purification was done using QIAquick PCR Purification kit (Qiagen Incorp, Valencia, CA) and 30 ng of these products were sequenced using BigDye Terminador v3. 1 Cycle Sequencing kit (Life Technologies Ltd, Paisley, U. K. ). The sequencing reaction products were analyzed on a ABI 3130xl Genetic Analyzer and using the Chromas Lite 2. 1. 1 software.
Kaplan-Meier curves and log-rank test were used to identify differences in LRFS, PFS, and CSS between groups of patients established. To analyze differences between two or more conditions we used the nonparametric Mann-Whitney U or Kruskal-Wallis tests, respectively. Statistical analyses were performed using the SPSS v. 14. 01 (IBM Corporation, Armonk, NY) and R v2. 15 (http://cran. es. r-project. org) software. Differences were considered significant at P-values <0. 05 in all the applied tests.
Low-RAB25 mRNA expression was associated with poor local recurrence-free, progression-free, and cancer-specific survival We confirmed the association between the expression of RAB25 and patient survival using an independent HNSCC microarray dataset previously reported by Rickman et al. [30]  (E-TABM-302). Cox model analysis showed that a high expression of RAB25 was associated with a higher patient survival (HR: 0. 64, CI 95% [0. 42-0. 97], P = 0. 03). We prospectively analyzed RAB25 mRNA expression levels by quantitative RT-PCR in 117 pretreatment tumor biopsies obtained from patients with locally advanced HNSCC treated at the HSCSP (Table  1 ). The median follow-up was 29 months. The median RAB25 mRNA expression was 3. 57 (range 0. 21-76. 58). Cox model analysis showed a significant association between RAB25 mRNA expression levels and survival (HR: 0.
HNSCC tumors express lower RAB25 levels than normal mucosa We compared RAB25 expression levels between 19 normal mucosa and 117 tumor samples. Tumor tissue had a lower expression of RAB25 than normal tissue (Fig. 3A ). We confirmed these results by analyzing RAB25 expression in three previously reported microarray datasets  [27, 28, 31]. We found significant differences in RAB25 mRNA expression levels between tumor and mucosa tissues in all the analyzed datasets. Tumor tissue showed a lower expression of RAB25 than normal tissue (Fig. 3B-D ). Analysis using the dataset described by Saintigny et al. [29]  (GSE26549) showed no significant differences in RAB25-mRNA levels of precursor HNSCC lesions with different histological grades (hyperplasia vs. dysplasia) (Fig. S2 ).
These patients could receive concomitant CRT as a first-line therapy, eliminating unnecessary surgery, and therefore increasing, organ preservation. Patients with a high probability of death, following a genotoxic treatment, could be candidates to surgery or an alternative treatment, avoiding the side effects of an ineffective genotoxic treatment. Although we analyzed RAB25 expression both retrospectively and prospectively, independent studies are needed to validate the sensitivity and specificity of this marker to predict treatment response. We also confirmed the prognostic value of RAB25 using an independent microarray dataset performed with surgical patients at different tumor stages, previously published by Rickman et al. A future independent study in patients receiving genotoxic therapy could allow us to validate the predictive value of RAB25.
e. , from isolated systems to the center of the Virgo cluster) and so it is ideal for environmental studies (see also  Hughes & Cortese 2009; Cortese & Hughes 2009). For the following analysis, two sub-samples are created based on the environment inhabited by each object in the sample. All galaxies with membership of the Virgo cluster, as determined with the criteria defined in  Gavazzi et al. (1999) , comprise the 'Virgo' sub-sample. Galaxies outside Virgo, which range from isolated systems to galaxies in groups, are selected for the 'non-Virgo' or 'field' sub-sample (and we refer to this sample of galaxies in low density environments using both terms interchangeably). A large multi-wavelength dataset is available for the HRS sample. For estimating oxygen abundances, we use flux measurements of optical emission lines presented in  Boselli et al.
(2012) , which discusses the data in detail. To briefly summarise, observations were carried out from 2004-2009 using the CARELEC spectrograph on the 1. 93 m telescope at Observatoire de Haute Provence. Galaxies were observed using a 5 arcmin slit of width of 2. 5 arcsec, adapted for the typical seeing conditions (2-3 arcsec), with a typical wavelength range of 3600-7000 Å incident on the CCD at a resolution of R∼1000. Integrated spectra were obtained for 135 HRS galaxies by observing in the drift-scan mode, whereby the spectrograph slit is allowed to drift across the galaxy major axis, and the data reduced using standard tasks in the IRAF 1 software package. By combining this data with emission line measurements from integrated spectra presented in  Gavazzi et al. (2004 ), Kennicutt (1992 ), Jansen et al.
(2000) ,  Moustakas & Kennicutt (2006)  and  Moustakas et al. (2010) , a homogeneous dataset of the spectral properties of 236 of the 260 HRS late-types has been assembled. The spectra are corrected for internal and galactic extinction and the Hα and Hβ lines are corrected for underlying stellar absorption. For Hβ lines with detected absorption, the absorption feature is deblended from the emission line using SPLOT. To be consistent with  Gavazzi et al. (2004) , a mean additive correction of 1. 8 in flux and -1. 4 Å in E. W. is applied to those Hβ lines where underlying absorption is not detected. For Hα lines, the spectral resolution and close proximity of the [Nii] lines prevent the measurement of the Hα underlying absorption. However, it has been shown that, despite quite different star formation histories, the underlying absorption of the Hα line is fairly constant with a mean value of E.
B and V band observations are available from the GOLDMine database  (Gavazzi et al. 2003)  for 182 of the 260 galaxies and morphologically averaged B -V colours are used when optical observations are unavailable. Morphologies are taken from the NASA/IPAC Extragalactic Database (NED). Bell et al. (2003)  report the errors in the mass-to-light ratio increase with B -V colour, typically between 0. 1 and 0. 2 dex for galaxies with blue optical colors. Including some error associated with the choice of IMF, our typical errors in stellar mass are <0. 3 dex. Observations from the GALEX  (Martin et al. 2005 ) GR2 to GR4 data releases in the near-ultraviolet (NUV; λ=2316 Å: ∆λ=1069 Å) band were available for all but one galaxy. NUV magnitudes were obtained by integrating the flux over the galaxy optical size, determined at the surface brightness of µ(B) = 25 mag arcsec -2 , with a typical uncertainty in the NUV photometry of ∼0.
Estimating metallicity In this section, we discuss in detail the new method adopted for estimating global oxygen abundances. In order to test the accuracy of the method and examine the relationships between metallicities derived from different calibrations, we include, in addition to the HRS sample, all available spectroscopic observations for galaxies in the GOLDMine database  (Gavazzi et al. 2003). The observations presented in  Gavazzi et al. (2004)  cover galaxies in the Virgo cluster and other nearby clusters -Coma and A1367, Cancer, A262 and Centaurus -plus 10 isolated objects, thereby increasing the number of observed galaxies to 478 objects. We stress the use of the 478 observations is restricted to testing our methodology in this section, whereas we use the HRS sample of 260 late-types for the main analysis.
This is encouraging when one considers that the two datasets were obtained in different ways: KE08 use spectroscopy from SDSS fibers, whereas this work uses integrated, drift-scan spectroscopy. The situation improves once the systematic discrepancies between the different calibrations are removed by converting the results into a base metallicity, such that all the abundance estimates from different calibrations are comparable. By fitting the relationship between the various results of the calibrations, KE08 were able to eliminate the systematic discrepancies and produce comparable metallicity measurements from different calibration methods. The conversion relations enabling a metallicity from one method to be converted into any other metallicity follow y = a + bx + cx 2  + dx 3 where y is the final or 'base' metallicity in 12+log(O/H) units, x is the original metallicity from an alternate calibration and a-d are the third order coefficients, as presented in Table  3  of KE08.
Following previous work, we define the gas fraction as the ratio of the gas mass to the sum of the gas and stellar mass, µ = M gas M * + M gas  (6)  such that it represents the amount of gas which has not yet been turned into stars. The gas mass is considered to be the mass of Hi (M HI ) with a correction for neutral helium M gas = 1. 32 M HI. We note that this is a 'hybrid' gas fraction, since it does not take into account the contributions of molecular hydrogen. The H 2 content comprises at least 20% of the total gas mass and should be given consideration. However, we currently lack a homogeneous dataset of CO maps for the HRS sample. There is also a large uncertainty of the CO-to-H 2 conversion factor, which varies with metallicity and other physical properties of the ISM in galaxies  (Boselli et al.
This suggests the scatter in the M-Z relation is more closely related to the gas content than SFR. This is an interesting result, as the correlations between stellar mass, metallicity and gas fraction found in this work hint that the available gas content is more important than the SFR. A relation between metallicity and gas fraction is expected if a galaxy evolves like a closed box, without the inflow or outflow of gas (see e. g. Edmunds 1990 ). As an additional test, we investigate the M * -Zµ plane. In order to compare it's scatter Fig. 4. Left panel: The relationship between stellar mass and metallicity for the HRS sample of galaxies (blue circles), using metallicity estimates based on the PP04 O3N2 base calibration. The KE08 best fit (solid black line) and our linear and polynomial fits (dashed red and solid green lines, respectively) to the binned data (black squares) are presented.
In addition, comparing the mean value in binned data for the cluster and field environments (right panel of Fig. 7 ), it is apparent that whilst the differences in the average metallicity estimates at each fixed stellar mass are of the order of 0. 05 dex, there is no systematic metallicity enhancement across the range of mass bins observed for either environment. Indeed, neither environment shows an enhanced metallicity in any two adjacent mass bins. Finally, using the field M-Z relation to calculate the residual metallicities for galaxies in both environments (lower panels of Fig. 7 ), we further demonstrate there is no significant difference between the cluster and field M-Z relations. Overall, the Virgo cluster members do not demonstrate a higher dispersion compared to the non-cluster galaxies.
Discussion In our analysis, we find that the gas content appears to drive the M-Z relation, given that we observe an anti-correlation between gas content and metal content, which remains even when removing the metallicity dependency on stellar mass. We also find no significant difference between M-Z relations of galaxies in cluster and field environments. Whilst the origin of the stellar mass -metallicity relation has been the focus of many previous studies, these new results are only made possible due to the unique dataset available for the HRS sample. Firstly, most previous studies have lacked spatially integrated, drift-scan spectra for a moderately-sized galaxy sample, instead using SDSS fiber spectroscopy or standard longslit spectroscopy (see e. g. Garnett 2002; Tremonti et al. 2004; Dalcanton 2007).
2011) , nor in those from past studies (e. g. Peeples & Shankar 2011). Thus, accurately explaining the Mµ relation remains an important goal for future work, as this will provide a constraint on the underlying physical processes governing the stellar, gas and metal content. Since the dataset presented in this paper will be useful for constraining models in future studies on chemical evolution, we therefore provide for the community the derived physical quantities of the 169 HRS late-types that have metallicity estimates (see Table  3 ).
Conclusions The aim of this work was to study the relationships between the stellar mass, metallicity, and gas content of galaxies in different environments. We used new optical, drift-scan spectroscopic observations of the HRS galaxy sample to obtain reliable metallicity estimates by taking the average oxygen abundances from five calibration methods. These measurements were combined with ultraviolet to near-infrared photometry and Hi 21 cm line observations to provide a multi-wavelength dataset capable of tracing the stellar, gas and metal content. We first demonstrated the reliability of our metallicity estimates and found further consistency between the stellar mass -metallicity relation observed in the HRS sample and those relations found in previous studies. A correlation was observed between the metal content and gas content of a galaxy, whereby gas poor galaxies are typically metal rich.
Acknowledgements. We gratefully acknowledge the constructive comments from the anonymous referee, which significantly improved the quality of this paper. TMH acknowledges the support of a  Kavli Fellowship. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( /FP7/2007-2013/)  under grant agreement No. 229517. This publication makes use of data from  2MASS , which is a joint project of the  University of Massachusetts  and the  IPAC/Caltech , funded by the  NASA  and the  NSF , and from the GALEX mission, developed in cooperation with the  CNES-France  and the  Korean Ministry of Science and Technology. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
Miscellaneous The 35 S-labeled proteins and protein complexes were detected by digital autoradiography (Storm imaging system; GE Healthcare) and analyzed using ImageQuant 5. 2 software (GE Healthcare). After Western blotting and immunodecoration with the indicated antibodies, the proteins were detected using a chemiluminescence kit (ECL; GE Healthcare). Quantification of Western blot signals was performed with ImageQuant 5. 2 software. concentration of 1 mg/ml and incubated with Ni 2+ -NTA agarose (Qiagen, Hilden, Germany) under constant rotation at 4°C. The Ni 2+ -NTA agarose beads were reisolated and washed intensively with lysis buffer containing 0. 1% (wt/vol) digitonin and 40 mM imidazole. Bound proteins were eluted with 250 mM imidazole and 0. 1% (wt/vol) digitonin in lysis buffer. For coimmunoprecipitation, anti-mtHsp70 antibodies were coupled covalently to protein A-Sepharose (GE Healthcare, Little Chalfront, UK) with dimethylpimelidate.
Previously selected precursor ions were dynamically excluded from fragmentation for 65 s. Mass spectrometric data analysis, including protein identification and SILAC-based relative protein quantification, was performed using MaxQuant, version 1. 2. 0. 18  (Cox and Mann, 2008) , and its integrated search engine Andromeda  (Cox et al. , 2011). For peptide and protein identification, MS/MS data were correlated with the Saccharomyces cerevisiae genome database (www. yeastgenome. org). The database search was performed with the following settings: mass tolerances of 7 ppm for precursor and 0. 5 D for fragment ions; tryptic specificity with a maximum of two missed cleavages;.
ACKNOWLEDGMENTS We thank  Nikolaus Pfanner  and  Peter Rehling  for discussion and  Conny Schütze  for material. We thank  Inge Perschil  and  Kurt Lobenwein  for expert technical assistance. This work was supported by the  Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 746 , the  Excellence Initiative of the German Federal and State Governments  ( EXC 294  BIOSS), and the  Alexander von Humboldt Stiftung.
These artifacts are particularly common in studies involving children or infants where the fNIRS cap is more loosely fastened on the head to increase participant tolerance of the method. A great deal of effort has been spent by researchers in developing better methods for identification and removal of motion artifacts, including principal component analysis  [9] , spline interpolation  [10] , wavelet transform  [11] , and Kalman filtering  [12]  based algorithms. These methods were compared by Cooper et. al. [13] , in which spline interpolation and wavelet based filtering gave the best results for two different performance metrics. As a note, these methods are applied in pre-processing of the fNIRS data and in general have some level of subjective tuning involved in the implementation of the algorithm.
In this work, we describe an iterative algorithm that utilizes an autoregressive model based pre-whitening filter and robust regression to reduce the effects of physiological and motion-induced sources of noise, respectively. We demonstrated that this algorithm has better performance than ordinary least squares with no motion correction, wavelet based motion correction  [11] , or spline interpolation based motion correction  [10]  in the presence of physiological noise and motion artifacts for both deconvolution (e. g. , HOMER  [9] ) and canonical (e. g. , SPM-NIRS  [14] ) based regression models. Our proposed algorithm is demonstrated for both synthetic and experimental data containing motion artifacts. In addition, we tested both short (event-related design) and long (block design) duration tasks as well as varying magnitudes of hemoglobin concentration changes (contrast to noise ratios).
For example, the tuning parameter could be decreased to the point of maintaining 85% statistical efficiency if more robustness against outliers is desired. The weighted least squares (WLS) solution is β = (X T F T W FX) -1 X T F T W Fy ( 11 ) where W is diagonal matrix of weights calculated from the filtered residuals as follows: [W ii ] = w ([Fy -FXβ ] i ). (12) Regression is done iteratively, updating the weight matrix W with each iteration until a convergence criterion is reached. As a practical note, robust regression is implemented as part of the statistics toolbox in Matlab (Mathworks R2012b, Natick MA) as the function "robustfit". The iteratively reweighted least squares estimator has an asymptotic covariance matrix given by cov(β ) = σ 2 E[ψ 2 (Fε/σ )] E[ψ ′ (Fε/σ )] 2 (X T F T FX) -1 , (13) where E denotes expection, and ψ is the influence function  [26] , which can be specified as ψ(r) = r w(r).
Perform iteratively reweighted least squares (IRLS). (a) Solve for β by WLS (Eq. (11a)). (b) Recalculate weight matrix W via Eq. (  12 ). (c) Repeat step 5a-b until changes in β are sufficiently small (e. g. , < 1% change). 6. Repeat steps 2-5 until changes in β are sufficiently small (e. g. , < 1% change). Note that it is much more computationally efficient to perform the filtering in step 4 via a digital filtering algorithm, such as the "filter" function in Matlab, rather than explicitly forming the convolution matrix F. The filtered data and design matrix can then be passed to any software implementation of IRLS, such as the "robustfit" function in Matlab. There are many approaches to estimating the AR model coefficients. The current default in Matlab is to minimize the squared prediction errors of the forward and reversed signals.