Colonization and invasion of a lake drawdown in the Mojave Desert

I examined the colonization patterns of plants in the drawdown zone of Lake Mead. There was an increase in the mean number of species over time as well as differences in the colonization patterns of two non-native plant species, Brassica tournefortii and Tamarix ramosissima. I also examined the impact of germination order on competition between B. tournefortii and four native species under controlled environmental conditions and found effects of germination order on both aboveground and belowground biomass for all species in the study. These results can aid management of the Lake Mead drawdown zone by providing an overview of how the community develops and where non-native species are likely to invade. Also, this study indicates that competitive relationships between the non-native B. tournefortii and four common native species should be considered in any efforts to control B. tournefortii

A Statistical approach to formant tracking

This thesis investigates a statistical approach to tracking formant trajectories in continuous speech. In this approach a probability measure is applied to a set of features extracted from each analysis frame of the speech signal, and a conditional mean estimate is used to determine formant frequency values. The features used can be vector quantization symbols, spectrum levels, or other sets of features related to formant frequencies. Continuity constraints can be applied via either simple smoothing algorithms or hidden Markov models. An example of this technique using a multivariate probability measure on LPC spectral values is examined in detail. A second example using vector quantization is also examined for comparison. The performance of these trackers under a variety of conditions is discussed

The Identification of CELSR3 and Other Potential Cell Surface Targets in Neuroendocrine Prostate Cancer.

UNLABELLED Although recent efforts have led to the development of highly effective androgen receptor (AR)-directed therapies for the treatment of advanced prostate cancer, a significant subset of patients will progress with resistant disease including AR-negative tumors that display neuroendocrine features [neuroendocrine prostate cancer (NEPC)]. On the basis of RNA sequencing (RNA-seq) data from a clinical cohort of tissue from benign prostate, locally advanced prostate cancer, metastatic castration-resistant prostate cancer and NEPC, we developed a multi-step bioinformatics pipeline to identify NEPC-specific, overexpressed gene transcripts that encode cell surface proteins. This included the identification of known NEPC surface protein CEACAM5 as well as other potentially targetable proteins (e.g., HMMR and CESLR3). We further showed that cadherin EGF LAG seven-pass G-type receptor 3 (CELSR3) knockdown results in reduced NEPC tumor cell proliferation and migration in vitro. We provide in vivo data including laser capture microdissection followed by RNA-seq data supporting a causal role of CELSR3 in the development and/or maintenance of the phenotype associated with NEPC. Finally, we provide initial data that suggests CELSR3 is a target for T-cell redirection therapeutics. Further work is now needed to fully evaluate the utility of targeting CELSR3 with T-cell redirection or other similar therapeutics as a potential new strategy for patients with NEPC. SIGNIFICANCE The development of effective treatment for patients with NEPC remains an unmet clinical need. We have identified specific surface proteins, including CELSR3, that may serve as novel biomarkers or therapeutic targets for NEPC

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Precision Medicine In The Age Of Big Data: Leveraging Machine Learning And Genomics For Drug Discovery

Targeted therapies designed to specifically target molecules involved in carcinogenesis have achieved remarkable antitumor efficacy. However resistance inevitably develops and many cancer patients are not candidates for these targeted therapies. Furthermore the clinical attrition rate continues to rise, which remains a barrier in the development of novel targeted therapies. Integration of extensive genomics datasets with large drug databases allows us to begin to tackle questions about target discovery and drug toxicity with the ultimate goal of accelerating personalized anticancer drug discovery. The purpose of this dissertation was to address these problems through the development of drug repurposing, toxicity prediction, and drug synergy prediction models. First to target the role of transcription factors as drivers of oncogenic activity, we developed a computational drug repositioning approach (CRAFTT) that makes predictions about drugs that specifically disrupt transcription factor activity. To do this, CRAFTT integrates transcription factor binding site information with drug-induced expression profiling. We found that CRAFTT was able to recover a significant number of known drug-transcription factor interactions and identified a novel interaction that we subsequently validated. Our work in drug discovery led us to ask questions about what makes a drug safe. We developed a data-driven approach (PrOCTOR) that integrates the properties of a compound’s targets and its structure to directly predict the likelihood of toxicity in clinical trials and was able to accurately classify known safe and toxic drugs. Finally to address the problem of drug resistance, we developed a machine learning approach to identify synergistic and effective drug combinations based on single drug efficacy information and limited drug combination testing. When applied to mutant BRAF melanoma, this approach exhibited significant predictive power upon evaluation with cross-validation and further experimental testing of previously untested drug combinations in cell lines independent of the training set. Altogether this work demonstrates how the integration of orthogonal datasets gives us power to address difficult questions that are critical for precision medicine and drug discovery. Approaches such as these have the potential to make a direct impact on how patients are treated, as well as to help prioritize and guide additional focused studies

CAP-EOM-CCSD Method with Smooth Voronoi CAP for Metastable Electronic States in Molecular Clusters

The complex absorbing potential (CAP) approach offers a practical tool for characterization of energies and lifetimes of metastable electronic states, such as temporary anions and core ionized states. Here, we present an implementation of the smooth Voronoi CAP combined with equation-of-motion coupled cluster with single and double substitutions method for metastable states. The performance of the smooth Voronoi and a standard box CAPs is compared for different classes of systems: resonances in isolated molecules and in molecular clusters. The results of the benchmark calculations indicate that the choice of the CAP shape should be guided by the character of the metastable states. While Voronoi CAPs yield stable results in the case of a resonance localized on one molecule, their performance in the cases of states delocalized over two or more molecular species can deteriorate due to the CAP leaking into the vacuum region between the moieties. <br /

The effect of temperature on the rate of egg quality deterioration during short holding periods

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Not availabl

Nuclear Spaces

(Statement of Responsibility) by Robert T. Gayvert(Thesis) Thesis (B.A.) -- New College of Florida, 1981(Electronic Access) RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE(Bibliography) Includes bibliographical references.(Source of Description) This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.(Local) Faculty Sponsor: Chae, Soo Bon

Predicting Cancer Prognosis Using Functional Genomics Data Sets

Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human cancers. Furthermore, we outline challenges that remain and future directions that may be explored to address them