Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC Santa Barbara Electronic Theses and Dissertations (2018)

State space models are powerful in modeling dynamic processes and at the same time have clear interpretations. Due to their flexibility and interpretability, mixed effects state space models have been studied in the literature for the modeling of multivariate longitudinal data.

In a multivariate mixed effects state space model, the population effects and subject random deviations of any variable can be modeled by different stochastic processes. These processes can differ between variables, allowing great flexibility in the modeling. In addition, the model provides multiple ways to characterize interactions between the variables. However, the expensive computational cost is a major hindrance to the application of the mixed effects state space model to data with large numbers of individuals. Let $m$ be the number of individuals. The current most efficient version of the Kalman filter, the univariate treatment, has time complexity O($m^3$) and space complexity O($m^2$). The univariate treatment can handle only a few hundred individuals at a high computational cost.

We discover special structures in the Kalman filter of the mixed effects state space model and develop a new algorithm to exploit these structures. This reduces both time and space complexity to O($m$) and enables easy modeling of hundreds of thousands of individuals without parallel computing, although it is also highly parallelizable.

We further extend the mixed effects state space model to a joint modeling framework, in which a mixed effects state space model characterizes longitudinal data and a logistic regression models the survival probability. The true values of the longitudinal variables, modeled by the latent state of the state space model, are used as predictors in the logistic regression.

Our joint model can (i) characterize the evolution of longitudinal variables and interactions between them, (ii) model the relationship between the survival probability and longitudinal variables/external covariates, and (iii) perform online predictions for longitudinal variables and survival probability.

We develop another efficient algorithm for the computation of the maximum likelihood estimates of parameters in the joint model with time and space complexity both linear in $m$.

    Cover page: Joint Modeling of Longitudinal and Survival Data via Multivariate Mixed Effects State Space Model
    • Article
    • Peer Reviewed
    UC Davis Previously Published Works (2022)
    Consumers and breeders value strawberry fruit with high sugar content and sweetness. However, many factors negatively affect this trait. bZIP-S1 subfamily was closely correlated with the carbohydrate metabolism. Moreover, it can regulate plant development and growth, and respond to various stress. In the present study, a total of four bZIP-S1 members were identified in strawberry. Quantitative RT-PCR analysis showed a member of this subfamily, designated as bZIP11, most highly expressed in different tissues of strawberry. Especially, it had a significant transcript accumulation during the fruit development and ripening. Constitutive expression of FvbZIP11 in tomato caused severe plant growth retardation which was accompanied by wrinkled and curly leaves. The leaf yellowish and chlorotic patches were reflected in decrease of chlorophyll. Meanwhile, epidermal cells were smaller in leaves of transgenic plant. In addition, the total soluble solid and soluble sugar content of the fruit was significantly higher than WT during the fruit ripening. Taken together, the heterologous overexpression of strawberry bZIP11 induced sugar accumulation and hindered plant growth in tomato, which provided more information for improving strawberry fruit quality.
      Cover page: Heterologous overexpression of strawberry bZIP11 induces sugar accumulation and inhibits plant growth of tomato
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2021)
      Multiple systematic reviews on greenspace and health outcomes exist, but the overall evidence base remains unclear. Therefore, we performed an umbrella review to collect and appraise all relevant systematic reviews of epidemiological studies on greenness exposure and health. We searched PubMed, Embase, and Web of Science from inception to June 28, 2021, and screened references of relevant articles. Systematic reviews with or without meta-analyses of epidemiological studies that examined the associations of greenness with any health outcome were included. Two independent investigators performed study selection and data extraction. We also evaluated the methodological quality of the included systematic reviews using the Assessing the Methodological Quality of Systematic Reviews 2 checklist. A total of 40 systematic reviews and meta-analyses were included, of which most were cross-sectional studies conducted in high-income countries. Greenspace exposure was estimated with various objective and subjective parameters. Beneficial associations of greenspace with all-cause and stroke-specific mortality, CVD morbidity, cardiometabolic factors, mental health, low birth weight, physical activity, sleep quality, and urban crime were observed. No consistent associations between greenspace and other health outcomes (e.g., cancers) were observed. Most of the included systematic reviews and meta-analyses had one or more limitations in methodology. Our findings provide supportive evidence regarding the beneficial effects of greenspace exposure on some aspects of human health. However, the credibility of such evidence was compromised by methodological limitations. Better performed systematic reviews and meta-analyses as well as longitudinal designed primary studies are needed to validate this conclusion.
        Cover page: Greenspace and human health: An umbrella review.
        • Article
        • Peer Reviewed
        UC Santa Cruz Previously Published Works (2023)
        Abstract. Marine diazotrophs convert dinitrogen (N2) gas into bioavailable nitrogen (N), supporting life in the global ocean. In 2012, the first version of the global oceanic diazotroph database (version 1) was published. Here, we present an updated version of the database (version 2), significantly increasing the number of in situ diazotrophic measurements from 13 565 to 55 286. Data points for N2 fixation rates, diazotrophic cell abundance, and nifH gene copy abundance have increased by 184 %, 86 %, and 809 %, respectively. Version 2 includes two new data sheets for the nifH gene copy abundance of non-cyanobacterial diazotrophs and cell-specific N2 fixation rates. The measurements of N2 fixation rates approximately follow a log-normal distribution in both version 1 and version 2. However, version 2 considerably extends both the left and right tails of the distribution. Consequently, when estimating global oceanic N2 fixation rates using the geometric means of different ocean basins, version 1 and version 2 yield similar rates (43–57 versus 45–63 Tg N yr−1; ranges based on one geometric standard error). In contrast, when using arithmetic means, version 2 suggests a significantly higher rate of 223±30 Tg N yr−1 (mean ± standard error; same hereafter) compared to version 1 (74±7 Tg N yr−1). Specifically, substantial rate increases are estimated for the South Pacific Ocean (88±23 versus 20±2 Tg N yr−1), primarily driven by measurements in the southwestern subtropics, and for the North Atlantic Ocean (40±9 versus 10±2 Tg N yr−1). Moreover, version 2 estimates the N2 fixation rate in the Indian Ocean to be 35±14 Tg N yr−1, which could not be estimated using version 1 due to limited data availability. Furthermore, a comparison of N2 fixation rates obtained through different measurement methods at the same months, locations, and depths reveals that the conventional 15N2 bubble method yields lower rates in 69 % cases compared to the new 15N2 dissolution method. This updated version of the database can facilitate future studies in marine ecology and biogeochemistry. The database is stored at the Figshare repository (https://doi.org/10.6084/m9.figshare.21677687; Shao et al., 2022).
          Creative Commons 'BY' version 4.0 license
          Top