Beclin 1 is a critical regulator of autophagy which functions as a scaffold protein to initiate autophagosome formation and maturation. Global Beclin 1 knockout mice are embryonically lethal, while heterozygous Beclin 1 mice display a reduction in autophagy and increased susceptibility to myocardial ischemia/reperfusion injury. Cre-mediated recombination is a powerful tool that is commonly used in research to generate tissue-specific and inducible knockout mouse lines. To specifically delete Beclin 1 in the adult heart, our lab has generated cardiac specific, inducible MerCreMer Beclin 1 f/f mice. In establishing this model, Cre toxicity (rapid cardiac dysfunction and increased mortality) was observed in Beclin1 f/f mice (BMCM) but not in MerCreMer cassette-carrying mice (MCM) upon injection with tamoxifen to induce Cre expression. Here, we assess Cre toxicity in Beclin1-deficiency in vitro and in vivo to determine whether the Beclin1 deficiency increases the sensitivity to Cre expression. We observed a dose-dependent toxicity of Cre induction in Beclin1-deficient MEFs and hearts. Overexpression of Cre did not cause cell death in Rab7-deficient MEFs or Beclin 1-deficient HeLa cells. Our findings suggest that lack of Beclin 1 leads to increased susceptibility to Cre-mediated toxicity.

The R120G mutation in alpha-b-crystallin (CryAB-R120G) causes a proteotoxic cardiomyopathy that is characterized by the accumulation of misfolded protein aggregates. Recently, mitochondria have been shown to import misfolded proteins as a novel mechanism of protein quality control in S. cerevisiae. However, this import mechanism has not been investigated in a mammalian system. Here we disrupt mitochondrial protein import to characterize the potential uptake of CryAB-R120G by mitochondria. We observed that mitochondrial import can be perturbed by knocking down a subunit of the translocase of the mitochondrial outer membrane (Tom20) and that this results in cytosolic accumulation of CryAB-R120G. Our findings suggest that mitochondria play a role in cytosolic proteostasis amid CryAB-R120G-induced aggregation.

High-precision measurements of large-scale cosmic structure are expected to revolutionize our understanding of fundamental physics, for example by the quantifying neutrino mass and elucidating the nature of dark energy. This dissertation tackles various of the challenges that must be faced in order to optimally extract information from cosmological surveys, taking a particular interest in neutrino mass.

Massive neutrinos suppress the growth of cosmic structure on small scales, where gravity is nonlinear. It is currently an urgent task to determine how to maximally retrieve information in the nonlinear regime, as a traditional power spectrum analysis is no longer optimal. We start by using simulations to investigate the amount of information regarding neutrino mass present in cosmic structure. We find that while there is in principle a lot of information, only a small fraction will be measurable by upcoming surveys such as DESI and LSST. This motivates the need to perform a combined analysis with other cosmological tracers, such as the cosmic microwave background, and galaxy peculiar velocities. We thus develop a Bayesian forward modeling framework to combine field-level inference with galaxy peculiar velocities as a means to optimally extract information.

Additionally, various numerical challenges arise due to the nonlinear effects of gravity. For example, computing the covariance matrices required for likelihood-based analyses becomes challenging, in particular due to the super-sample covariance effect. Moreover, the inference often involves non-trivial posterior surfaces which are plagued by volume effects such as the Look-Elsewhere Effect. This is particularly prevalent when searching for evidence of exotic new models. In the latter chapters of the dissertation we provide methods to solve both of these problems.

All put together, this dissertation provides the ingredients for the cosmology community to move closer to an optimal measurement of neutrino mass and other cosmological parameters.

Over the past years, light detection and ranging (lidar) systems have been vastly investigated and commercialized for various platforms such as self-driving cars, unmanned aerial vehicles, and spacecraft. A wide range of lidar applications such as topographical imaging, remote object sensing, oceanographic and atmospheric surveillance, navigation, and driver assistance mostly relies on range measurements. The majority of the lidar instrumentation used in these applications operates in a pulsed time-of-flight (PToF) mode to acquire the range information by comparing the delay between the ejection time of an optical impulse and the detection time of the pulse in flight. Inherently, this conventional approach lacks the capability of simultaneous velocimetry and fine-ranging resolution requires high-speed detection electronics. Moreover, continuous-wave (CW) laser-based lidar systems are being investigated. CW lidars are desired because they use coherent detection techniques that provide a higher signal-to-noise ratio (SNR) than the direct detection method used in PToF. Up to date, various CW lidar techniques such as amplitude-modulated continuous wave (AMCW) lidars, frequency modulated continuous wave (FMCW) lidars, and phase-based ranging have been demonstrated. Hence, to perform the measurements and to extract the precise distance of the target, the AMCW method requires sweeping of amplitude and phase, while the FMCW technique requires frequency sweeping. As an alternative to the existing lidar technologies, Multi-Tone Continuous-Wave (MTCW) lidar is developed, which eliminates the necessity of any form of phase, frequency, or amplitude sweeping in a CW lidar configuration. In the MTCW approach, multiple fixed phase-locked radio frequencies (RF) are utilized to modulate a CW laser by using Mach-Zehnder modulators. A portion of the light is kept as a local oscillator to realize coherent detection to enhance the SNR. The modulated beam is transmitted to free space and the echo signal is collected by the receiver of the lidar. Each sideband accumulates a different phase based on the frequency and target distance. It is possible to compute the target distance by either converting the phase differences into RF tone power variations or by exploiting the phase and frequency differences between RF tones. Three versions of the MTCW lidar are constructed based on the utilization of tone amplitudes or phases, namely amplitude-base MTCW (AB-MTCW), phase-enhanced amplitude-based MTCW (PE-MTCW), and phase-based MTCW (PB-MTCW) lidars. Theoretical modeling, numerical simulations, and experimental verifications are presented for each technique. For AB-MTCW lidar, <1cm ranging precision with 0.8cm/s velocimetry resolution is demonstrated. Furthermore, the PE-MTCW approach is matured to provide a solution to fast targets ranging via AB-MTCW lidar. Finally, the PB-MTCW methodology is developed that can perform the ranging of dynamic or static targets at a range that is ×500 larger than the coherence length of the CW laser with a <1cm ranging resolution and precision. It is also shown that, by integrating the modulated beam with a quasi-CW pulsation, it is possible to perform ranging beyond the unambiguous range of the CW lidar, as well. Therefore, the PB-MTCW lidar can be considered an alternative long-distance remote sensing device for aerial or space-based altimetry applications.

Statistical modeling of infectious disease data is among the oldest applications of statistics. Today, it is an increasingly relevant application of research, due to globalization that enables diseases to spread further and faster, as well as the abundance of relevant data from electronic surveillance systems, seroprevalence studies, and genetic sequencing of pathogens. In this work, we develop novel statistical methods to combine varied data sources to improve both inference and forecasting. First, we work with data from assay validation studies and active surveillance studies to develop confidence intervals for prevalence estimates from complex surveys with imperfect assays. In this complicated setting, there are no established competitive methods, and ours exhibits at least nominal coverage. In addition, we apply our model in simplified cases where competitors exist and demonstrate desirable properties. Next, we develop a semi-parametric Bayesian compartmental model that effectively integrates passively collected time series of diagnostic tests and mortality data, as well as actively collected seroprevalence data. We emphasize retrospective inference and evaluate the utility of each data stream in the context of short-term forecasting. Finally, we focus on healthcare demand forecasting during epidemic surges of pathogen variants capable of immune escape. We build upon our Bayesian compartmental model to incorporate time series of cases, hospitalizations, ICU admissions, deaths, and genetic sequence counts. We show that using genetic information leads to superior forecasting performance, compared to traditional models. Throughout each project, we employ our methods to analyze a variety of COVID-19 data sets at the county, state, and national levels.

From the original definition of a set whose jump is as simple as possible (A' =_T 0'), to more recent definitions involving randomness, notions of lowness appear throughout recursion theory. In that spirit, a non-recursive set A will be said to be low for speed if for any recursive set R and any computation of R from A, there is an oracle-free computation of R that is no more than polynomial-time slower than the A-computation.

We will construct such an r.e. set and discuss some properties of these sets. We will show that promptly simple r.e. sets cannot be low for speed, and also that there are non-prompt sets that are not low for speed. We conclude by showing that generic sets are low for speed if and only if P = NP.

WRC project W-727 focuses on evaluating the capacity of selected aquatic plant species for low concentrations of herbicide and nitrate removal from nursery waste waters. Two nurseries in the Sacramento area were selected as test sites. From June 1988 through October 1989, water samples from inflow and outflow water from both nurseries were collected and analysed for inorganic constituents and herbicides. Of the herbicides analyzed (simazine, oryzalin, oxyfluorfen and pendimethalin), only oryzalin and oxyfluorten were present in measurable quantities. Pendimethalin was never detected and simazine appeared in detectable quantities only twice. Both nitrate and ammonia nitrogen concentrations were consistently high in outflow from both nurseries, except for the period of about three months during the winter. Soluble reactive phosphorus was relatively low.