159 research outputs found
High-precision buffer circuit for suppression of regenerative oscillation
Precision analog signal conditioning electronics have been developed for wind tunnel model attitude inertial sensors. This application requires low-noise, stable, microvolt-level DC performance and a high-precision buffered output. Capacitive loading of the operational amplifier output stages due to the wind tunnel analog signal distribution facilities caused regenerative oscillation and consequent rectification bias errors. Oscillation suppression techniques commonly used in audio applications were inadequate to maintain the performance requirements for the measurement of attitude for wind tunnel models. Feedback control theory is applied to develop a suppression technique based on a known compensation (snubber) circuit, which provides superior oscillation suppression with high output isolation and preserves the low-noise low-offset performance of the signal conditioning electronics. A practical design technique is developed to select the parameters for the compensation circuit to suppress regenerative oscillation occurring when typical shielded cable loads are driven
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
Very long chain fatty acid metabolism is required in acute myeloid leukemia
Acute myeloid leukemia (AML) cells have an atypical metabolic phenotype characterized by increased mitochondrial mass, as well as a greater reliance on oxidative phosphorylation and fatty acid oxidation (FAO) for survival. To exploit this altered metabolism, we assessed publicly available databases to identify FAO enzyme overexpression. Very long chain acyl-CoA dehydrogenase (VLCAD; ACADVL) was found to be overexpressed and critical to leukemia cell mitochondrial metabolism. Genetic attenuation or pharmacological inhibition of VLCAD hindered mitochondrial respiration and FAO contribution to the tricarboxylic acid cycle, resulting in decreased viability, proliferation, clonogenic growth, and AML cell engraftment. Suppression of FAO at VLCAD triggered an increase in pyruvate dehydrogenase activity that was insufficient to increase glycolysis but resulted in adenosine triphosphate depletion and AML cell death, with no effect on normal hematopoietic cells. Together, these results demonstrate the importance of VLCAD in AML cell biology and highlight a novel metabolic vulnerability for this devastating disease
Sex and Gender Differences in Travel-Associated Disease
Background. No systematic studies exist on sex and gender differences across a broad range of travel-associated diseases. Methods. Travel and tropical medicine GeoSentinel clinics worldwide contributed prospective, standardized data on 58,908 patients with travel-associated illness to a central database from 1 March 1997 through 31 October 2007. We evaluated sex and gender differences in health outcomes and in demographic characteristics. Statistical significance for crude analysis of dichotomous variables was determined using hi; 2 tests with calculation of odds ratios (ORs) and 95% confidence intervals (CIs). The main outcome measure was proportionate morbidity of specific diagnoses in men and women. The analyses were adjusted for age, travel duration, pretravel encounter, reason for travel, and geographical region visited. Results. We found statistically significant (Pµ.001) differences in morbidity by sex. Women are proportionately more likely than men to present with acute diarrhea (OR, 1.13; 95% CI, 1.09-1.38), chronic diarrhea (OR, 1.28; 95% CI, 1.19-1.37), irritable bowel syndrome (OR, 1.39; 95% CI, 1.24-1.57), upper respiratory tract infection (OR, 1.23; 95% CI, 1.14-1.33); urinary tract infection (OR, 4.01; 95% CI, 3.34-4.71), psychological stressors (OR, 1.3; 95% CI, 1.14-1.48), oral and dental conditions, or adverse reactions to medication. Women are proportionately less likely to have febrile illnesses (OR, 0.15; 95% CI, 0.10-0.21); vector-borne diseases, such as malaria (OR, 0.46; 95% CI, 0.41-0.51), leishmaniasis, or rickettsioses (OR, 0.57; 95% CI, 0.43-0.74); sexually transmitted infections (OR, 0.68; 95% CI 0.58-0.81); viral hepatitis (OR, 0.34; 95% CI, 0.21-0.54); or noninfectious problems, including cardiovascular disease, acute mountain sickness, and frostbite. Women are statistically significantly more likely to obtain pretravel advice (OR, 1.28; 95% CI, 1.23-1.32), and ill female travelers are less likely than ill male travelers to be hospitalized (OR, 0.45; 95% CI, 0.42-0.49). Conclusions. Men and women present with different profiles of travel-related morbidity. Preventive travel medicine and future travel medicine research need to address gender-specific intervention strategies and differential susceptibility to diseas
Approaches for advancing scientific understanding of macrosystems
The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them
Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
We apply machine learning in the form of a nearest neighbor instance-based
algorithm (NN) to generate full photometric redshift probability density
functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky
Survey (SDSS DR5). We use a conceptually simple but novel application of NN to
generate the PDFs - perturbing the object colors by their measurement error -
and using the resulting instances of nearest neighbor distributions to generate
numerous individual redshifts. When the redshifts are compared to existing SDSS
spectroscopic data, we find that the mean value of each PDF has a dispersion
between the photometric and spectroscopic redshift consistent with other
machine learning techniques, being sigma = 0.0207 +/- 0.0001 for main sample
galaxies to r < 17.77 mag, sigma = 0.0243 +/- 0.0002 for luminous red galaxies
to r < ~19.2 mag, and sigma = 0.343 +/- 0.005 for quasars to i < 20.3 mag. The
PDFs allow the selection of subsets with improved statistics. For quasars, the
improvement is dramatic: for those with a single peak in their probability
distribution, the dispersion is reduced from 0.343 to sigma = 0.117 +/- 0.010,
and the photometric redshift is within 0.3 of the spectroscopic redshift for
99.3 +/- 0.1% of the objects. Thus, for this optical quasar sample, we can
virtually eliminate 'catastrophic' photometric redshift estimates. In addition
to the SDSS sample, we incorporate ultraviolet photometry from the Third Data
Release of the Galaxy Evolution Explorer All-Sky Imaging Survey (GALEX AIS GR3)
to create PDFs for objects seen in both surveys. For quasars, the increased
coverage of the observed frame UV of the SED results in significant improvement
over the full SDSS sample, with sigma = 0.234 +/- 0.010. We demonstrate that
this improvement is genuine. [Abridged]Comment: Accepted to ApJ, 10 pages, 12 figures, uses emulateapj.cl
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