Life Sciences Commons^™

Impacts Of Long-Term Precipitation Manipulation On Hydraulic Architecture, Xylem Function, And Canopy Status In A Piñon-Juniper Woodland, Patrick J. Hudson

Biology ETDs

The Southwestern US is predicted to become hotter and drier, as global climate change forces increasing temperatures and variability in timing and size of precipitation inputs. Drought stress has become more frequent in recent decades, and resulted in massive forest mortality in piñon-juniper woodlands. During recent severe droughts (2000-2003, 2009-2012), piñon pine (Pinus edulis Englem.) suffered disproportionately high mortality compared to co-occurring one-seed juniper (Juniperus monosperma [Engelm.] Sarg.). A large-scale precipitation manipulation experiment was established in a piñon-juniper woodland in central New Mexico to test hypotheses regarding tree survival and mortality with respect to altered water regimes. Our …

Development Of In Vivo Systems For Detecting And Studying Ribosome Inhibition By Small Molecules, Shijie Huang

Chemistry and Chemical Biology ETDs

The ribosome is the quintessential antibacterial drug target, with many structurally and mechanistically distinct classes of antibacterial agents acting by inhibiting ribosome function. Detecting and quantifying ribosome inhibition by small molecules and investigating their binding modes and mechanisms of action are critical to antibacterial drug discovery and development efforts. To develop a ribosome inhibition assay that is operationally simple, yet provides direct information on the drug target and the mechanism of action, we have developed engineered E. coli strains harboring an orthogonal ribosome controlled green fluorescent protein reporter that produce fluorescent signal when the O-ribosome is inhibited. As a proof …

Network Inference Driven Drug Discovery, Gergely Zahoránszky-Kőhalmi, Tudor I. Oprea Md, Phd, Cristian G. Bologa Phd, Subramani Mani Md, Phd, Oleg Ursu Phd

Biomedical Sciences ETDs

The application of rational drug design principles in the era of network-pharmacology requires the investigation of drug-target and target-target interactions in order to design new drugs. The presented research was aimed at developing novel computational methods that enable the efficient analysis of complex biomedical data and to promote the hypothesis generation in the context of translational research. The three chapters of the Dissertation relate to various segments of drug discovery and development process.

The first chapter introduces the integrated predictive drug discovery platform „SmartGraph”. The novel collaborative-filtering based algorithm „Target Based Recommender (TBR)” was developed in the framework of this …

Engineering Small Molecule Based Dimerization To Induce Translation And Provide Optogenetic Control, Catherine Wright

Chemistry and Chemical Biology ETDs

We wanted to develop a system that combines the spatial control of photoactivation and control of translation to build a tool to spatially control translation in neurons. This kind of tool could be used to investigate the role of spatially controlled translation of any protein in neural behavior. In this way the development and growth of neural processes could be studied to elucidate the mechanisms for spatially sensitive events such as pathfinding, repair, or long-term potentiation.

Chemically induced dimerization was used to install a switch into the activation of translation for specific genes. An abscisic acid (ABA) dependent dimerization of …

Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm

Computer Science ETDs

Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …