The angiogenic factor midkine is regulated by dexamethasone and retinoic acid during alveolarization and in alveolar epithelial cells

<p>Abstract</p> <p>Background</p> <p>A precise balance exists between the actions of endogenous glucocorticoids (GC) and retinoids to promote normal lung development, in particular during alveolarization. The mechanisms controlling this balance are largely unknown, but recent evidence suggests that midkine (MK), a retinoic acid-regulated, pro-angiogenic growth factor, may function as a critical regulator. The purpose of this study was to examine regulation of MK by GC and RA during postnatal alveolar formation in rats.</p> <p>Methods</p> <p>Newborn rats were treated with dexamethasone (DEX) and/or all-trans-retinoic acid (RA) during the first two weeks of life. Lung morphology was assessed by light microscopy and radial alveolar counts. MK mRNA and protein expression in response to different treatment were determined by Northern and Western blots. In addition, MK protein expression in cultured human alveolar type 2-like cells treated with DEX and RA was also determined.</p> <p>Results</p> <p>Lung histology confirmed that DEX treatment inhibited and RA treatment stimulated alveolar formation, whereas concurrent administration of RA with DEX prevented the DEX effects. During normal development, MK expression was maximal during the period of alveolarization from postnatal day 5 (PN5) to PN15. DEX treatment of rat pups decreased, and RA treatment increased lung MK expression, whereas concurrent DEX+RA treatment prevented the DEX-induced decrease in MK expression. Using human alveolar type 2 (AT2)-like cells differentiated in culture, we confirmed that DEX and cAMP decreased, and RA increased MK expression.</p> <p>Conclusion</p> <p>We conclude that MK is expressed by AT2 cells, and is differentially regulated by corticosteroid and retinoid treatment in a manner consistent with hormonal effects on alveolarization during postnatal lung development.</p

The Berkeley Sample of Stripped-Envelope Supernovae

We present the complete sample of stripped-envelope supernova (SN) spectra observed by the Lick Observatory Supernova Search (LOSS) collaboration over the last three decades: 888 spectra of 302 SNe, 652 published here for the first time, with 384 spectra (of 92 SNe) having photometrically-determined phases. After correcting for redshift and Milky Way dust reddening and reevaluating the spectroscopic classifications for each SN, we construct mean spectra of the three major spectral subtypes (Types IIb, Ib, and Ic) binned by phase. We compare measures of line strengths and widths made from this sample to the results of previous efforts, confirming that O I {\lambda}7774 absorption is stronger and found at higher velocity in Type Ic SNe than in Types Ib or IIb SNe in the first 30 days after peak brightness, though the widths of nebular emission lines are consistent across subtypes. We also highlight newly available observations for a few rare subpopulations of interest.Comment: 13 pages; 14 figures; 3 tables. Accepted for publication in MNRA

3′-End Sequencing for Expression Quantification (3SEQ) from Archival Tumor Samples

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3′-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (∼9.6K genes) and FFPET (∼8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (∼4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research

Unsupervised reduction of random noise in complex data by a row-specific, sorted principal component-guided method

<p>Abstract</p> <p>Background</p> <p>Large biological data sets, such as expression profiles, benefit from reduction of random noise. Principal component (PC) analysis has been used for this purpose, but it tends to remove small features as well as random noise.</p> <p>Results</p> <p>We interpreted the PCs as a mere signal-rich coordinate system and sorted the squared PC-coordinates of each row in descending order. The sorted squared PC-coordinates were compared with the distribution of the ordered squared random noise, and PC-coordinates for insignificant contributions were treated as random noise and nullified. The processed data were transformed back to the initial coordinates as noise-reduced data. To increase the sensitivity of signal capture and reduce the effects of stochastic noise, this procedure was applied to multiple small subsets of rows randomly sampled from a large data set, and the results corresponding to each row of the data set from multiple subsets were averaged. We call this procedure Row-specific, Sorted PRincipal component-guided Noise Reduction (RSPR-NR). Robust performance of RSPR-NR, measured by noise reduction and retention of small features, was demonstrated using simulated data sets. Furthermore, when applied to an actual expression profile data set, RSPR-NR preferentially increased the correlations between genes that share the same Gene Ontology terms, strongly suggesting reduction of random noise in the data set.</p> <p>Conclusion</p> <p>RSPR-NR is a robust random noise reduction method that retains small features well. It should be useful in improving the quality of large biological data sets.</p

The latency-associated transcript locus of herpes simplex virus 1 is a virulence determinant in human skin

Herpes simplex virus 1 (HSV-1) infects skin and mucosal epithelial cells and then travels along axons to establish latency in the neurones of sensory ganglia. Although viral gene expression is restricted during latency, the latency-associated transcript (LAT) locus encodes many RNAs, including a 2 kb intron known as the hallmark of HSV-1 latency. Here, we studied HSV-1 infection and the role of the LAT locus in human skin xenografts in vivo and in cultured explants. We sequenced the genomes of our stock of HSV-1 strain 17syn+ and seven derived viruses and found nonsynonymous mutations in many viral proteins that had no impact on skin infection. In contrast, deletions in the LAT locus severely impaired HSV-1 replication and lesion formation in skin. However, skin replication was not affected by impaired intron splicing. Moreover, although the LAT locus has been implicated in regulating gene expression in neurones, we observed only small changes in transcript levels that were unrelated to the growth defect in skin, suggesting that its functions in skin may be different from those in neurones. Thus, although the LAT locus was previously thought to be dispensable for lytic infection, we show that it is a determinant of HSV-1 virulence during lytic infection of human skin

Supernovae and radio transients in M 82

We present optical and near-infrared (IR) photometry and near-IR spectroscopy of SN 2004am, the only optically detected supernova (SN) in M 82. These demonstrate that SN 2004am was a highly reddened type II-P SN similar to the low luminosity type II-P events such as SNe 1997D and 2005cs. We show that SN 2004am was located coincident with the obscured super star cluster M 82-L, and from the cluster age infer a progenitor mass of 12 +7/-3 Msun. In addition to this, we present a high spatial resolution Gemini-N K-band adaptive optics image of the site of SN 2008iz and a second transient of uncertain nature, both detected so far only at radio wavelengths. Using image subtraction techniques together with archival data from the Hubble Space Telescope, we are able to recover a near-IR transient source co-incident with both objects. We find the likely extinction towards SN 2008iz to be not more than Av ~ 10. The nature of the second transient remains elusive and we regard an extremely bright microquasar in M 82 as the most plausible scenario.Comment: 14 pages, 8 figures, accepted for publication in MNRA

Optical and infrared observations of the Type IIP SN2002hh from day 3 to 397

We present optical and infrared (IR) observations of the type IIP SN2002hh from 3 to 397 days after explosion. The optical spectroscopic (4-397d) and photometric (3-278d) data are complemented by spectroscopic (137-381d) and photometric (137-314d) data acquired at IR wavelengths. This is the first time L-band spectra have ever been successfully obtained for a supernova at a distance beyond the Local Group. The VRI light curves in the first 40 days reveal SN2002hh to be a SN IIP (plateau) - the most common of all core-collapse supernovae. SN2002hh is one of the most highly extinguished supernovae ever investigated. To provide a good match between its early-time spectrum and a coeval spectrum of the Type IIP SN1999em, as well as maintaining consistency with KI interstellar absorption, we invoke a 2-component extinction model. One component is due to the combined effect of the interstellar medium of our Milky Way Galaxy and the SN host galaxy, while the other component is due to a "dust pocket" where the grains have a mean size smaller than in the interstellar medium. The early-time optical light curves of SNe 1999em and 2002hh are generally well-matched, as are the radioactive tails of these two SNe and SN1987A. The late-time similarity of the SN2002hh optical light curves to those of SN1987A, together with measurements of the optical/IR luminosity and [FeII] 1.257mu emission indicate that 0.07 +- 0.02 Msun of Ni 56 was ejected by SN2002hh. [... ABRIDGED...] From the [OI] 6300,6364 A doublet luminosity we infer a 16-18 Msun main-sequence progenitor star. The progenitor of SN2002hh was probably a red supergiant with a substantial, dusty wind.Comment: 32 pages, 30 figures, accepted for publication in MNRA

Berkeley Supernova Ia Program I: Observations, Data Reduction, and Spectroscopic Sample of 582 Low-Redshift Type Ia Supernovae

In this first paper in a series we present 1298 low-redshift (z\leq0.2) optical spectra of 582 Type Ia supernovae (SNe Ia) observed from 1989 through 2008 as part of the Berkeley SN Ia Program (BSNIP). 584 spectra of 199 SNe Ia have well-calibrated light curves with measured distance moduli, and many of the spectra have been corrected for host-galaxy contamination. Most of the data were obtained using the Kast double spectrograph mounted on the Shane 3 m telescope at Lick Observatory and have a typical wavelength range of 3300-10,400 Ang., roughly twice as wide as spectra from most previously published datasets. We present our observing and reduction procedures, and we describe the resulting SN Database (SNDB), which will be an online, public, searchable database containing all of our fully reduced spectra and companion photometry. In addition, we discuss our spectral classification scheme (using the SuperNova IDentification code, SNID; Blondin & Tonry 2007), utilising our newly constructed set of SNID spectral templates. These templates allow us to accurately classify our entire dataset, and by doing so we are able to reclassify a handful of objects as bona fide SNe Ia and a few other objects as members of some of the peculiar SN Ia subtypes. In fact, our dataset includes spectra of nearly 90 spectroscopically peculiar SNe Ia. We also present spectroscopic host-galaxy redshifts of some SNe Ia where these values were previously unknown. [Abridged]Comment: 34 pages, 11 figures, 11 tables, revised version, re-submitted to MNRAS. Spectra will be released in January 2013. The SN Database homepage (http://hercules.berkeley.edu/database/index_public.html) contains the full tables, plots of all spectra, and our new SNID template

Gender Differences in Russian Colour Naming

In the present study we explored Russian colour naming in a web-based psycholinguistic experiment (http://www.colournaming.com). Colour singletons representing the Munsell Color Solid (N=600 in total) were presented on a computer monitor and named using an unconstrained colour-naming method. Respondents were Russian speakers (N=713). For gender-split equal-size samples (NF=333, NM=333) we estimated and compared (i) location of centroids of 12 Russian basic colour terms (BCTs); (ii) the number of words in colour descriptors; (iii) occurrences of BCTs most frequent non-BCTs. We found a close correspondence between females’ and males’ BCT centroids. Among individual BCTs, the highest inter-gender agreement was for seryj ‘grey’ and goluboj ‘light blue’, while the lowest was for sinij ‘dark blue’ and krasnyj ‘red’. Females revealed a significantly richer repertory of distinct colour descriptors, with great variety of monolexemic non-BCTs and “fancy” colour names; in comparison, males offered relatively more BCTs or their compounds. Along with these measures, we gauged denotata of most frequent CTs, reflected by linguistic segmentation of colour space, by employing a synthetic observer trained by gender-specific responses. This psycholinguistic representation revealed females’ more refined linguistic segmentation, compared to males, with higher linguistic density predominantly along the redgreen axis of colour space