University of Colorado Boulder

About: University of Colorado Boulder is a education organization based out in Boulder, Colorado, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 48794 authors who have published 115151 publications receiving 5387328 citations. The organization is also known as: CU Boulder & UCB.

Recent advances in dynamic covalent chemistry

Abstract: Dynamic covalent chemistry (DCvC) has been strongly integrated into diverse research fields, and has enabled easy access to a variety of combinatorial libraries, 2-D macrocycles, and 3-D molecular cages that target many important applications, such as drug discovery, biotechnology, molecular separation, light harvesting, etc. DCvC relies on the reversible formation and breaking of rather strong covalent bonding within molecules. Therefore it combines the error-correction capability of supramolecular chemistry and the robustness of covalent bonding. Compared to those supramolecular interactions, dynamic covalent reactions usually have slower kinetics and require the assistance of catalysts to achieve rapid equilibrium. Although the scope of dynamic covalent reactions is rapidly expanding, the reversible reactions suitable for DCvC are still very limited. The identification and development of new dynamic reactions and catalysts would be critical for the further advancement of DCvC. This review covers the recent development of dynamic covalent reactions as well as their applications.

Environmental and stoichiometric controls on microbial carbon‐use efficiency in soils

Abstract: Summary Carbon (C) metabolism is at the core of ecosystem function. Decomposers play a critical role in this metabolism as they drive soil C cycle by mineralizing organic matter to CO2. Their growth depends on the carbon-use efficiency (CUE), defined as the ratio of growth over C uptake. By definition, high CUE promotes growth and possibly C stabilization in soils, while low CUE favors respiration. Despite the importance of this variable, flexibility in CUE for terrestrial decomposers is still poorly characterized and is not represented in most biogeochemical models. Here, we synthesize the theoretical and empirical basis of changes in CUE across aquatic and terrestrial ecosystems, highlighting common patterns and hypothesizing changes in CUE under future climates. Both theoretical considerations and empirical evidence from aquatic organisms indicate that CUE decreases as temperature increases and nutrient availability decreases. More limited evidence shows a similar sensitivity of CUE to temperature and nutrient availability in terrestrial decomposers. Increasing CUE with improved nutrient availability might explain observed declines in respiration from fertilized stands, while decreased CUE with increasing temperature and plant C : N ratios might decrease soil C storage. Current biogeochemical models could be improved by accounting for these CUE responses along environmental and stoichiometric gradients.

Repetitive Elements May Comprise Over Two-Thirds of the Human Genome

Abstract: Transposable elements (TEs) are conventionally identified in eukaryotic genomes by alignment to consensus element sequences. Using this approach, about half of the human genome has been previously identified as TEs and low-complexity repeats. We recently developed a highly sensitive alternative de novo strategy, P-clouds, that instead searches for clusters of high-abundance oligonucleotides that are related in sequence space (oligo “clouds”). We show here that P-clouds predicts >840 Mbp of additional repetitive sequences in the human genome, thus suggesting that 66%–69% of the human genome is repetitive or repeat-derived. To investigate this remarkable difference, we conducted detailed analyses of the ability of both P-clouds and a commonly used conventional approach, RepeatMasker (RM), to detect different sized fragments of the highly abundant human Alu and MIR SINEs. RM can have surprisingly low sensitivity for even moderately long fragments, in contrast to P-clouds, which has good sensitivity down to small fragment sizes (∼25 bp). Although short fragments have a high intrinsic probability of being false positives, we performed a probabilistic annotation that reflects this fact. We further developed “element-specific” P-clouds (ESPs) to identify novel Alu and MIR SINE elements, and using it we identified ∼100 Mb of previously unannotated human elements. ESP estimates of new MIR sequences are in good agreement with RM-based predictions of the amount that RM missed. These results highlight the need for combined, probabilistic genome annotation approaches and suggest that the human genome consists of substantially more repetitive sequence than previously believed.

Mapping target signatures via partial unmixing of AVIRIS data

Abstract: A complete spectral unmixing of a complicated AVIRIS scene may not always be possible or even desired. High quality data of spectrally complex areas are very high dimensional and are consequently difficult to fully unravel. Partial unmixing provides a method of solving only that fraction of the data inversion problem that directly relates to the specific goals of the investigation. Many applications of imaging spectrometry can be cast in the form of the following question: 'Are my target signatures present in the scene, and if so, how much of each target material is present in each pixel?' This is a partial unmixing problem. The number of unmixing endmembers is one greater than the number of spectrally defined target materials. The one additional endmember can be thought of as the composite of all the other scene materials, or 'everything else'. Several workers have proposed partial unmixing schemes for imaging spectrometry data, but each has significant limitations for operational application. The low probability detection methods described by Farrand and Harsanyi and the foreground-background method of Smith et al are both examples of such partial unmixing strategies. The new method presented here builds on these innovative analysis concepts, combining their different positive attributes while attempting to circumvent their limitations. This new method partially unmixes AVIRIS data, mapping apparent target abundances, in the presence of an arbitrary and unknown spectrally mixed background. It permits the target materials to be present in abundances that drive significant portions of the scene covariance. Furthermore it does not require a priori knowledge of the background material spectral signatures. The challenge is to find the proper projection of the data that hides the background variance while simultaneously maximizing the variance amongst the targets.