Assessing the influence of abiotic factors and leaf-level properties on the stability of growing-season canopy greenness in a deciduous forest

Maps depicting spatial pattern in the stability of summer greenness could advance understanding of how forest ecosystems will respond to global changes such as a longer growing season. Declining summer greenness, or “greendown”, is spectrally related to declining near-infrared reflectance and is observed in most remote sensing time series to begin shortly after peak greenness at the end of spring and extend until the beginning of leaf coloration in autumn,. Understanding spatial patterns in the strength of greendown has recently become possible with the advancement of Landsat phenology products, which show that greendown patterns vary at scales appropriate for linking these patterns to proposed environmental forcing factors. This study tested two non-mutually exclusive hypotheses for how leaf measurements and environmental factors correlate with greendown and decreasing NIR reflectance across sites. At the landscape scale, we used linear regression to test the effects of maximum greenness, elevation, slope, aspect, solar irradiance and canopy rugosity on greendown. Secondly, we used leaf chemical traits and reflectance observations to test the effect of nitrogen availability and intrinsic water use efficiency on leaf-level greendown, and landscape-level greendown measured from Landsat. The study was conducted using Quercus alba canopies across 21 sites of an eastern deciduous forest in North America between June and August 2014. Our linear model explained greendown variance with an R2=0.47 with maximum greenness as the greatest model effect. Subsequent models excluding one model effect revealed elevation and aspect were the two topographic factors that explained the greatest amount of greendown variance. Regression results also demonstrated important interactions between all three variables, with the greatest interaction showing that aspect had greater influence on greendown at sites with steeper slopes. Leaf-level reflectance was correlated with foliar δ13C (proxy for intrinsic water use efficiency), but foliar δ13C did not translate into correlations with landscape-level variation in greendown from Landsat. Therefore, we conclude that Landsat greendown is primarily indicative of landscape position, with a small effect of canopy structure, and no measureable effect of leaf reflectance. With this understanding of Landsat greendown we can better explain the effects of landscape factors on vegetation reflectance and perhaps on phenology, which would be very useful for studying phenology in the context of global climate change

Intercepting Cyclic Dinucleotide Signaling with Small Molecules

Bacterial infections, especially the ones that are caused by multidrug-resistant strains, are becoming increasingly difficult to treat and put enormous stress on healthcare systems. Recently President Obama announced a new initiative to combat the growing problem of antibiotic resistance. New types of antibiotic drugs are always in need to catch up with the rapid speed of bacterial drug-resistance acquisition. Bacterial second messengers, cyclic dinucleotides, play important roles in signal transduction and therefore are currently generating great buzz in the microbiology community because it is believed that small molecules that inhibit cyclic dinucleotide signaling could become next-generation antibacterial agents. The first identified cyclic dinucleotide, c-di-GMP, has now been shown to regulate a large number of processes, such as virulence, biofilm formation, cell cycle, quorum sensing, etc. Recently, another cyclic dinucleotide, c-di-AMP, has emerged as a regulator of key processes in Gram-positive and mycobacteria. C-di-AMP is now known to regulate DNA damage sensing, fatty acid synthesis, potassium ion transport, cell wall homeostasis and host type I interferon response induction. Due to the central roles that cyclic dinucleotides play in bacteria, we are interested in small molecules that intercept cyclic dinucleotide signaling with the hope that these molecules would help us learn more details about cyclic dinucleotide signaling or could be used to inhibit bacterial viability or virulence. This dissertation documents the development of several small molecule inhibitors of a cyclic dinucleotide synthase (DisA from B. subtilis) and phosphodiesterases (RocR from P. aeruginosa and CdnP from M. tuberculosis). We also demonstrate that an inhibitor of RocR PDE can inhibit bacterial swarming motility, which is a virulence factor.

Sparse Signal Representation in Digital and Biological Systems

Theories of sparse signal representation, wherein a signal is decomposed as the sum of a small number of constituent elements, play increasing roles in both mathematical signal processing and neuroscience. This happens despite the differences between signal models in the two domains. After reviewing preliminary material on sparse signal models, I use work on compressed sensing for the electron tomography of biological structures as a target for exploring the efficacy of sparse signal reconstruction in a challenging application domain. My research in this area addresses a topic of keen interest to the biological microscopy community, and has resulted in the development of tomographic reconstruction software which is competitive with the state of the art in its field. Moving from the linear signal domain into the nonlinear dynamics of neural encoding, I explain the sparse coding hypothesis in neuroscience and its relationship with olfaction in locusts. I implement a numerical ODE model of the activity of neural populations responsible for sparse odor coding in locusts as part of a project involving offset spiking in the Kenyon cells. I also explain the validation procedures we have devised to help assess the model's similarity to the biology. The thesis concludes with the development of a new, simplified model of locust olfactory network activity, which seeks with some success to explain statistical properties of the sparse coding processes carried out in the network.