646 research outputs found
Assessment of pulmonary edema: principles and practice
Pulmonary edema increasingly is recognized as a perioperative complication affecting outcome. Several risk factors have been identified, including those of cardiogenic origin, such as heart failure or excessive fluid administration, and those related to increased pulmonary capillary permeability secondary to inflammatory mediators.
Effective treatment requires prompt diagnosis and early intervention. Consequently, over the past 2 centuries a concentrated effort to develop clinical tools to rapidly diagnose pulmonary edema and track response to treatment has occurred. The ideal properties of such a tool would include high sensitivity and specificity, easy availability, and the ability to diagnose early accumulation of lung water before the development of the full clinical presentation. In addition, clinicians highly value the ability to precisely quantify extravascular lung water accumulation and differentiate hydrostatic from high permeability etiologies of pulmonary edema.
In this review, advances in understanding the physiology of extravascular lung water accumulation in health and in disease and the various mechanisms that protect against the development of pulmonary edema under physiologic conditions are discussed. In addition, the various bedside modalities available to diagnose early accumulation of extravascular lung water and pulmonary edema, including chest auscultation, chest roentgenography, lung ultrasonography, and transpulmonary thermodilution, are examined. Furthermore, advantages and limitations of these methods for the operating room and intensive care unit that are critical for proper modality selection in each individual case are explored
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
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SMART-1 Impact Ground-based campaign
Based on predictions of impact magnitude and cloud ejecta dynamics, we organized a SMART-1 ground-based observation campaign to perform coordinated measurements of the impact. Results from the coordinated multi-site campaign will be discussed
Dietary soy and meat proteins induce distinct physiological and gene expression changes in rats
This study reports on a comprehensive comparison of the effects of soy and meat proteins given at the recommended level on physiological markers of metabolic syndrome and the hepatic transcriptome. Male rats were fed semi-synthetic diets for 1 wk that differed only regarding protein source, with casein serving as reference. Body weight gain and adipose tissue mass were significantly reduced by soy but not meat proteins. The insulin resistance index was improved by soy, and to a lesser extent by meat proteins. Liver triacylglycerol contents were reduced by both protein sources, which coincided with increased plasma triacylglycerol concentrations. Both soy and meat proteins changed plasma amino acid patterns. The expression of 1571 and 1369 genes were altered by soy and meat proteins respectively. Functional classification revealed that lipid, energy and amino acid metabolic pathways, as well as insulin signaling pathways were regulated differently by soy and meat proteins. Several transcriptional regulators, including NFE2L2, ATF4, Srebf1 and Rictor were identified as potential key upstream regulators. These results suggest that soy and meat proteins induce distinct physiological and gene expression responses in rats and provide novel evidence and suggestions for the health effects of different protein sources in human diets
Galaxy Clustering in Early SDSS Redshift Data
We present the first measurements of clustering in the Sloan Digital Sky
Survey (SDSS) galaxy redshift survey. Our sample consists of 29,300 galaxies
with redshifts 5,700 km/s < cz < 39,000 km/s, distributed in several long but
narrow (2.5-5 degree) segments, covering 690 square degrees. For the full,
flux-limited sample, the redshift-space correlation length is approximately 8
Mpc/h. The two-dimensional correlation function \xi(r_p,\pi) shows clear
signatures of both the small-scale, ``fingers-of-God'' distortion caused by
velocity dispersions in collapsed objects and the large-scale compression
caused by coherent flows, though the latter cannot be measured with high
precision in the present sample. The inferred real-space correlation function
is well described by a power law, \xi(r)=(r/6.1+/-0.2 Mpc/h)^{-1.75+/-0.03},
for 0.1 Mpc/h < r < 16 Mpc/h. The galaxy pairwise velocity dispersion is
\sigma_{12} ~ 600+/-100 km/s for projected separations 0.15 Mpc/h < r_p < 5
Mpc/h. When we divide the sample by color, the red galaxies exhibit a stronger
and steeper real-space correlation function and a higher pairwise velocity
dispersion than do the blue galaxies. The relative behavior of subsamples
defined by high/low profile concentration or high/low surface brightness is
qualitatively similar to that of the red/blue subsamples. Our most striking
result is a clear measurement of scale-independent luminosity bias at r < 10
Mpc/h: subsamples with absolute magnitude ranges centered on M_*-1.5, M_*, and
M_*+1.5 have real-space correlation functions that are parallel power laws of
slope ~ -1.8 with correlation lengths of approximately 7.4 Mpc/h, 6.3 Mpc/h,
and 4.7 Mpc/h, respectively.Comment: 51 pages, 18 figures. Replaced to match accepted ApJ versio
Origin of structural difference in metabolic networks with respect to temperature
<p>Abstract</p> <p>Background</p> <p>Metabolism is believed to adaptively shape-shift with changing environment. In recent years, a structural difference with respect to temperature, which is an environmental factor, has been revealed in metabolic networks, implying that metabolic networks transit with temperature. Subsequently, elucidatation of the origin of these structural differences due to temperature is important for understanding the evolution of life. However, the origin has yet to be clarified due to the complexity of metabolic networks.</p> <p>Results</p> <p>Consequently, we propose a simple model with a few parameters to explain the transitions. We first present mathematical solutions of this model using mean-field approximation, and demonstrate that this model can reproduce structural properties, such as heterogeneous connectivity and hierarchical modularity, in real metabolic networks both qualitatively and quantitatively. We next show that the model parameters correlate with optimal growth temperature. In addition, we present a relationship between multiple cyclic properties and optimal growth temperature in metabolic networks.</p> <p>Conclusion</p> <p>From the proposed model, we find that such structural properties are determined by the emergence of a short-cut path, which reduces the minimum distance between two nodes on a graph. Furthermore, we investigate correlations between model parameters and growth temperature; as a result, we find that the emergence of the short-cut path tends to be inhibited with increasing temperature. In addition, we also find that the short-cut path bypasses a relatively long path at high temperature when the emergence of the new path is not inhibited. Even further, additional network analysis provides convincing evidence of the reliability of the proposed model and its conclusions on the possible origins of differences in metabolic network structure.</p
An Extended Gene Protein/Products Boolean Network Model Including Post-Transcriptional Regulation
Background: Networks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs. Results: In this paper we show how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN. The enhanced BN model is implemented in a software toolkit (EBNT) that allows to analyze boolean GRNs from both a structural and a dynamic point of view. The open-source toolkit is compatible with available visualization tools like Cytoscape and allows to run detailed analysis of the network topology as well as of its attractors, trajectories, and state-space. In the paper, a small GRN built around the mTOR gene is used to demonstrate the main capabilities of the toolkit. Conclusions: The extended model proposed in this paper opens new opportunities in the study of gene regulation. Several of the successful researches done with the support of BN to understand high-level characteristics of regulatory networks, can now be improved to better understand the role of post-transcriptional regulation for example as a network-wide noise-reduction or stabilization mechanism
The Fifth Data Release of the Sloan Digital Sky Survey
This paper describes the Fifth Data Release (DR5) of the Sloan Digital Sky
Survey (SDSS). DR5 includes all survey quality data taken through June 2005 and
represents the completion of the SDSS-I project (whose successor, SDSS-II will
continue through mid-2008). It includes five-band photometric data for 217
million objects selected over 8000 square degrees, and 1,048,960 spectra of
galaxies, quasars, and stars selected from 5713 square degrees of that imaging
data. These numbers represent a roughly 20% increment over those of the Fourth
Data Release; all the data from previous data releases are included in the
present release. In addition to "standard" SDSS observations, DR5 includes
repeat scans of the southern equatorial stripe, imaging scans across M31 and
the core of the Perseus cluster of galaxies, and the first spectroscopic data
from SEGUE, a survey to explore the kinematics and chemical evolution of the
Galaxy. The catalog database incorporates several new features, including
photometric redshifts of galaxies, tables of matched objects in overlap regions
of the imaging survey, and tools that allow precise computations of survey
geometry for statistical investigations.Comment: ApJ Supp, in press, October 2007. This paper describes DR5. The SDSS
Sixth Data Release (DR6) is now public, available from http://www.sdss.or
The Seventh Data Release of the Sloan Digital Sky Survey
This paper describes the Seventh Data Release of the Sloan Digital Sky Survey
(SDSS), marking the completion of the original goals of the SDSS and the end of
the phase known as SDSS-II. It includes 11663 deg^2 of imaging data, with most
of the roughly 2000 deg^2 increment over the previous data release lying in
regions of low Galactic latitude. The catalog contains five-band photometry for
357 million distinct objects. The survey also includes repeat photometry over
250 deg^2 along the Celestial Equator in the Southern Galactic Cap. A
coaddition of these data goes roughly two magnitudes fainter than the main
survey. The spectroscopy is now complete over a contiguous area of 7500 deg^2
in the Northern Galactic Cap, closing the gap that was present in previous data
releases. There are over 1.6 million spectra in total, including 930,000
galaxies, 120,000 quasars, and 460,000 stars. The data release includes
improved stellar photometry at low Galactic latitude. The astrometry has all
been recalibrated with the second version of the USNO CCD Astrograph Catalog
(UCAC-2), reducing the rms statistical errors at the bright end to 45
milli-arcseconds per coordinate. A systematic error in bright galaxy photometr
is less severe than previously reported for the majority of galaxies. Finally,
we describe a series of improvements to the spectroscopic reductions, including
better flat-fielding and improved wavelength calibration at the blue end,
better processing of objects with extremely strong narrow emission lines, and
an improved determination of stellar metallicities. (Abridged)Comment: 20 pages, 10 embedded figures. Accepted to ApJS after minor
correction
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