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