University of Texas at Austin

About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.

Molecular Imaging of Biological Samples: Localization of Peptides and Proteins Using MALDI-TOF MS

Abstract: Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) has been used to generate ion images of samples in one or more mass-to-charge (m/z) values, providing the capability of mapping specific molecules to two-dimensional coordinates of the original sample. The high sensitivity of the technique (low-femtomole to attomole levels for proteins and peptides) allows the study of organized biochemical processes occurring in, for example, mammalian tissue sections. The mass spectrometer is used to determine the molecular weights of the molecules in the surface layers of the tissue. Molecules desorbed from the sample typically are singly protonated, giving an ion at (M + H)+, where M is the molecular mass. The procedure involves coating the tissue section, or a blotted imprint of the section, with a thin layer of energy-absorbing matrix and then analyzing the sample to produce an ordered array of mass spectra, each containing nominal m/z values typically covering a range of over 50 000 Da. Images...

Mapping Species Distributions: Spatial Inference and Prediction

Abstract: Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.

Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples

Abstract: Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift samples (z< 0.1), all three seasons from the SDSS-II (0.05

Profitability of Momentum Strategies: An Evaluation of Alternative Explanations

Abstract: This paper evaluates various explanations for the profitability of momentum strategies documented in Jegadeesh and Titman ~1993!. The evidence indicates that momentum profits have continued in the 1990s, suggesting that the original results were not a product of data snooping bias. The paper also examines the predictions of recent behavioral models that propose that momentum profits are due to delayed overreactions that are eventually reversed. Our evidence provides support for the behavioral models, but this support should be tempered with caution. Many portfolio managers and stock analysts subscribe to the view that momentum strategies yield significant profits. Jegadeesh and Titman ~1993! examine a variety of momentum strategies and document that strategies that buy stocks with high returns over the previous 3 to 12 months and sell stocks with poor returns over the same time period earn profits of about one percent per month for the following year. 1 Although these results have been well accepted, the source of the profits and the interpretation of the evidence are widely debated. Although some have argued that the results provide strong evidence of “market inefficiency,” others have argued that the returns from these strategies are either compensation for risk, or alternatively, the product of data mining. The criticism that observed empirical regularities arise because of data mining is typically the hardest to address because empirical research in nonexperimental settings is limited by data availability. Fortunately, with