296 research outputs found
Bayesian modeling and inference for asymmetric responses with applications
University of Minnesota Ph.D. dissertation. July 2017. Major: Biostatistics. Advisors: Dipankar Bandyopadhyay, Lynn Eberly. 1 computer file (PDF); x, 129 pages.Analysis of asymmetric data poses several unique challenges. In this thesis, we propose a series of parametric models under the Bayesian hierarchical framework to account for asymmetry (arising from non-Gaussianity, tail behavior, etc) in both continuous and discrete response data. First, we model continuous asymmetric responses assuming normal random errors by using a dynamic linear model discretized from a differential equation which absorbs the asymmetry from the data generation mechanism. We then extend the skew-normal/independent parametric family to accommodate spatial clustering and non-random missingness observed in asymmetric continuous responses, and demonstrate its utility in obtaining precise parameter estimates and prediction in presence of skewness and thick-tails. Finally, under a latent variable formulation, we use a generalized extreme value (GEV) link to model multivariate asymmetric spatially-correlated binary responses that also exhibit non-random missingness, and show how this proposal improves inference over other popular alternative link functions in terms of bias and prediction. We assess our proposed method via simulation studies and two real data analyses on public health. Using simulated data, we investigate the performance of the proposed method to accurately accommodate asymmetry along with other data features such as spatial dependency and non-random missingness simultaneously, leading to precise posterior parameter estimates. Regarding data illustrations, we first validate the efficiency in using differential equations to handle skewed exposure assessment responses derived from an occupational hygiene study. Furthermore, we also conduct efficient risk evaluation of various covariates on periodontal disease responses from a dataset on oral epidemiology. The results from our investigation re-establishes the significance of moving away from the normality assumption and instead consider pragmatic distributional assumptions on the random model terms for efficient Bayesian parameter estimation under a unified framework with a variety of data complexities not earlier considered in the two aforementioned areas of public health research
Sector Similarity in Input-Output Networks
Input-Output (IO) model is a macroeconomic model describing the inter-sectoral
interdependence of economies. It is widely used to analyze environmental impacts from
economic activities. The conventional method to build up the IO table is largely based
on onerous data collection but simple linear approximation. In order to more accurately
construct IO tables and efficiently capture outliers among the dataset, we introduce
network theories to investigate the underlying relationships between economic sectors.
By probing into similarity between economic sectors, we could conclude correlations
and connection patterns between individual economic flows. In this way, even with
partial data of one IO table available, it will still be possible to restore the complete
map of an IO table by referencing their inherited relationships. The achievement of such
prediction will further advance our environmental analysis that based upon IO model
via more accurate and up-to-data data. This study focuses on similarity exploration
between economic sectors in IO model and constructing a theoretical framework for
establishing IO table using network theories of link prediction.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/110981/1/Sector Similarity in Input-Output Networks (Xiaoyue Zhao)_2015.pd
Boosting with stumps for predicting transcription start sites
Promoter prediction is a difficult but important problem in gene finding, and it is critical for elucidating the regulation of gene expression. We introduce a new promoter prediction program, CoreBoost, which applies a boosting technique with stumps to select important small-scale as well as large-scale features. CoreBoost improves greatly on locating transcription start sites. We also demonstrate that by further utilizing some tissue-specific information, better accuracy can be achieved
Improving immunogenicity and safety of flagellin as vaccine carrier by high-density display on virus-like particle surface
Flagellin is a protein-based adjuvant that activates toll-like receptor (TLR) 5. Flagellin has been actively explored as vaccine adjuvants and carriers. Preclinical and clinical studies find flagellin-based vaccines have a risk to induce systemic adverse reactions potentially due to its overt activation of TLR5. To improve safety and immunogenicity of flagellin as vaccine carriers, FljB was displayed at high densities on hepatitis b core (HBc) virus-like particle (VLP) surface upon c/e1 loop insertion. FljB-HBc (FH) VLPs showed significantly reduced ability to activate TLR5 or induce systemic interleukin-6 release as compared to FljB. FH VLPs also failed to significantly increase rectal temperature of mice, while FljB could significantly increase rectal temperature of mice. These data indicated systemic safety of FljB could be significantly improved by high-density display on HBc VLP surface. Besides improved safety, FH VLPs and FljB similarly boosted co-administered ovalbumin immunization and FH VLPs were found to induce two-fold higher anti-FljB antibody titer than FljB. These data indicated preserved adjuvant potency and improved immunogenicity after high-density display of FljB on HBc VLP surface. Consistent with the high immunogenicity, FH VLPs were found to be more efficiently taken up by bone marrow-derived dendritic cells and stimulate more potent dendritic cell maturation than FljB. Lastly, FH VLPs were found to be a more immunogenic carrier than FljB, HBc VLPs, or the widely used keyhole limpet hemocyanin for nicotine vaccine development with a good local and systemic safety. Our data support FH VLPs to be a potentially safer and more immunogenic carrier than FljB for vaccine development
A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups
As infected and vaccinated population increases, some countries decided not
to impose non-pharmaceutical intervention measures anymore and to coexist with
COVID-19. However, we do not have a comprehensive understanding of its
consequence , especially for China where most population has not been infected
and most Omicron transmissions are silent. This paper serves as the first study
to reveal the complete silent transmission dynamics of COVID-19 overlaying a
big data of more than 0.7 million real individual mobility tracks without any
intervention measures throughout a week in a Chinese city, with an extent of
completeness and realism not attained in existing studies. Together with the
empirically inferred transmission rate of COVID-19, we find surprisingly that
with only 70 citizens to be infected initially, 0.33 million becomes infected
silently at last. We also reveal a characteristic daily periodic pattern of the
transmission dynamics, with peaks in mornings and afternoons. In addition,
retailing, catering and hotel staff are more likely to get infected than other
professions. Unlike all other age groups and professions, elderly and retirees
are more likely to get infected at home than outside home.Comment: 39 pages, 5+9 figure
Characterizing the temporally stable structure of community evolution in intra-urban origin-destination networks
Intra-urban origin-destination (OD) network communities evolve throughout the
day, indicating changing groups of closely connected regions. Under this
variation, groups of regions with high consistency of community affiliation
characterize the temporally stable structure of the evolution process, aiding
in comprehending urban dynamics. However, how to quantify this consistency and
identify these groups are open questions. In this study, we introduce the
consensus OD network to quantify the consistency of community affiliation among
regions. Furthermore, the temporally stable community decomposition method is
proposed to identify groups of regions with high internal and low external
consistency (named "stable groups"), where each group consists of temporally
stable cores and attaching peripheries. Wuhan taxi data is used to verify our
methods. On the hourly time scale, eleven stable groups containing 82.9% of
regions are identified. This high percentage suggests that dynamic communities
can be well organized via cores. Moreover, stable groups are spatially closed
and more likely to distribute within a single district and separated by water
bodies. Cores exhibit higher POI entropy and more healthcare and shopping
services than peripheries. Our methods and empirical findings contribute to
some practical issues, such as urban area division, polycentric evaluation and
construction, and infectious disease control
Stabilization and destabilization of hybrid systems by periodic stochastic controls
This paper aims to determine whether or not a periodic stochastic feedback control can stabilize or destabilize a given nonlinear hybrid system. New methods are developed and sufficient conditions on the stability and instability for hybrid nonlinear systems with periodic stochastic perturbations are provided. These results are then used to examine stabilization and destabilization by periodic stochastic feedback controls, including intermittent stochastic controls
TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge
This paper describes the TSUP team's submission to the ISCSLP 2022
conversational short-phrase speaker diarization (CSSD) challenge which
particularly focuses on short-phrase conversations with a new evaluation metric
called conversational diarization error rate (CDER). In this challenge, we
explore three kinds of typical speaker diarization systems, which are spectral
clustering(SC) based diarization, target-speaker voice activity
detection(TS-VAD) and end-to-end neural diarization(EEND) respectively. Our
major findings are summarized as follows. First, the SC approach is more
favored over the other two approaches under the new CDER metric. Second, tuning
on hyperparameters is essential to CDER for all three types of speaker
diarization systems. Specifically, CDER becomes smaller when the length of
sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP
will worsen the CDER metric on the challenge data. Our submitted SC system
eventually ranks the third place in the challenge
- …