Effects of pH on Growth of Salvinia molesta Mitchell

Growth of giant salvinia ( Salvinia molesta Mitchell) under different pH regimes was examined at the Lewisville Aquatic Ecosystem Research Facility (LAERF) in Lewisville, Texas.(PDF has 5 pages.

Transcription activator like effector (TALE)-directed piggyBac transposition in human cells.

Insertional therapies have shown great potential for combating genetic disease and safer methods would undoubtedly broaden the variety of possible illness that can be treated. A major challenge that remains is reducing the risk of insertional mutagenesis due to random insertion by both viral and non-viral vectors. Targetable nucleases are capable of inducing double-stranded breaks to enhance homologous recombination for the introduction of transgenes at specific sequences. However, off-target DNA cleavages at unknown sites can lead to mutations that are difficult to detect. Alternatively, the piggyBac transposase is able perform all of the steps required for integration; therefore, cells confirmed to contain a single copy of a targeted transposon, for which its location is known, are likely to be devoid of aberrant genomic modifications. We aimed to retarget transposon insertions by comparing a series of novel hyperactive piggyBac constructs tethered to a custom transcription activator like effector DNA-binding domain designed to bind the first intron of the human CCR5 gene. Multiple targeting strategies were evaluated using combinations of both plasmid-DNA and transposase-protein relocalization to the target sequence. We demonstrated user-defined directed transposition to the CCR5 genomic safe harbor and isolated single-copy clones harboring targeted integrations

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl

High Resolution Chandra Spectroscopy of Gamma Cassiopeia (B0.5IVe)

gamma Cas has long been famous for its unique hard X-ray characteristics. We report herein on a 53 ks Chandra HETGS observation of this target. An inspection of our spectrum shows that it is quite atypical for a massive star, with abnormally weak Fe XXV, XXVI lines, Ly-alpha lines of H-like species from Fe XVII, XXIII, XXIV, S XVI, Si XIV, Mg XII, Ne X, O VII, VIII, and N VII. Also, line ratios of the rif-triplet of for a few He-like ions XVII are consistent with the dominance of collisional atomic processes. Yet, the presence of Fe and Si fluorescence K features indicates that photoionization also occurs in nearby cold gas. The line profiles indicate a mean velocity at rest and a broadening of 500 km/s. A global fitting analysis of the line and continuum spectrum finds that there are 3-4 plasma emission components. The dominant hot (12 keV) component and has a Fe abundance of 0.22 solar. Some fraction of this component (10-30%) is heavily absorbed. The other 2-3 components, with temperatures 0.1, 0.4, 3 keV, are "warm," have a nearly solar composition, a lower column absorption, and are responsible for most other emission lines. The strength of the fluorescence features and the dual-column absorption model for the hot plasma component suggest the presence near the hot sites of a cold gas structure with a column density of 10^23 cm^-2. Since this value is consistent with theoretical estimates of the vertical disk column of this star, these attributes suggest that the X-rays originate near the star or disk. It is possible that the Fe anomaly in the hot component is related to the First Ionization Potential effect found in coronal structures around active cool stars. This would be yet another indication that the X-rays -rays are produced in the immediate vicinity of the Be star.Comment: 32 pages, 4 figures (Fig. 3 colorized.) To be published in 01/10/04 Astrophysical Journal, Main Journal; included figures and updated formattin

Cigarette Smoke Initiates Oxidative Stress-Induced Cellular Phenotypic Modulation Leading to Cerebral Aneurysm Pathogenesis.

OBJECTIVE: Cigarette smoke exposure (CSE) is a risk factor for cerebral aneurysm (CA) formation, but the molecular mechanisms are unclear. Although CSE is known to contribute to excess reactive oxygen species generation, the role of oxidative stress on vascular smooth muscle cell (VSMC) phenotypic modulation and pathogenesis of CAs is unknown. The goal of this study was to investigate whether CSE activates a NOX (NADPH oxidase)-dependent pathway leading to VSMC phenotypic modulation and CA formation and rupture. APPROACH AND RESULTS: In cultured cerebral VSMCs, CSE increased expression of NOX1 and reactive oxygen species which preceded upregulation of proinflammatory/matrix remodeling genes (MCP-1, MMPs [matrix metalloproteinase], TNF-α, IL-1β, NF-κB, KLF4 [Kruppel-like factor 4]) and downregulation of contractile genes (SM-α-actin [smooth muscle α actin], SM-22α [smooth muscle 22α], SM-MHC [smooth muscle myosin heavy chain]) and myocardin. Inhibition of reactive oxygen species production and knockdown of NOX1 with siRNA or antisense decreased CSE-induced upregulation of NOX1 and inflammatory genes and downregulation of VSMC contractile genes and myocardin. p47phox-/- NOX knockout mice, or pretreatment with the NOX inhibitor, apocynin, significantly decreased CA formation and rupture compared with controls. NOX1 protein and mRNA expression were similar in p47phox-/- mice and those pretreated with apocynin but were elevated in unruptured and ruptured CAs. CSE increased CA formation and rupture, which was diminished with apocynin pretreatment. Similarly, NOX1 protein and mRNA and reactive oxygen species were elevated by CSE, and in unruptured and ruptured CAs. CONCLUSIONS: CSE initiates oxidative stress-induced phenotypic modulation of VSMCs and CA formation and rupture. These molecular changes implicate oxidative stress in the pathogenesis of CAs and may provide a potential target for future therapeutic strategies

A versatile ligation-independent cloning method suitable for high-throughput expression screening applications

This article describes the construction of a set of versatile expression vectors based on the In-Fusion™ cloning enzyme and their use for high-throughput cloning and expression screening. Modifications to commonly used vectors rendering them compatible with In-Fusion™ has produced a ligation-independent cloning system that is (1) insert sequence independent (2) capable of cloning large PCR fragments (3) efficient over a wide (20-fold) insert concentration range and (4) applicable to expression in multiple hosts. The system enables the precise engineering of (His6-) tagged constructs with no undesirable vector or restriction-site-derived amino acids added to the expressed protein. The use of a multiple host-enabled vector allows rapid screening in both E. coli and eukaryotic hosts (HEK293T cells and insect cell hosts, e.g. Sf9 cells). These high-throughput screening activities have prompted the development and validation of automated protocols for transfection of mammalian cells and Ni-NTA protein purification

Structure of the PII signal transduction protein of Neisseria meningitidis at 1.85 Å resolution

The structure of the PII signal transduction protein of N. meningitidis at 1.85 Å resolution is described

Class Discovery in Galaxy Classification

In recent years, automated, supervised classification techniques have been fruitfully applied to labeling and organizing large astronomical databases. These methods require off-line classifier training, based on labeled examples from each of the (known) object classes. In practice, only a small batch of labeled examples, hand-labeled by a human expert, may be available for training. Moreover, there may be no labeled examples for some classes present in the data, i.e. the database may contain several unknown classes. Unknown classes may be present due to 1) uncertainty in or lack of knowledge of the measurement process, 2) an inability to adequately ``survey'' a massive database to assess its content (classes), and/or 3) an incomplete scientific hypothesis. In recent work, new class discovery in mixed labeled/unlabeled data was formally posed, with a proposed solution based on mixture models. In this work we investigate this approach, propose a competing technique suitable for class discovery in neural networks, and evaluate both methods for classification and class discovery on several astronomical data sets. Our results demonstrate up to a 57% reduction in classification error compared to a standard neural network classifier that uses only labeled data