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Open Access Publications from the University of California

Scholarly Works (25 results)

  • Article
  • Peer Reviewed
2020 Publications (2020)
    Cover page: A Qualitative Analysis of Vehicle Positioning Requirements for Connected Vehicle Applications
    • Thesis
    • Peer Reviewed
    UC Riverside Electronic Theses and Dissertations (2019)

    Connected and Automated Vehicles (CAVs) have the potential to greatly improve roadway safety, mobility, and associated environmental factors. To date, a large number of CAV applications have been developed and tested, both in the real world and in simulation. In nearly all cases, it is assumed that vehicle localization is sufficiently accurate at all times. Only a few studies have accounted for uncertainty in vehicle localization, which can be significant given CAVs’ strong reliance on Global Navigation Satellite Systems (GNSS)-based systems for positioning. Positioning accuracy can vary both in time and space and is sometimes quite poor (>10 meters) in urban and other challenging environments for GNSS. This is a problem given that many CAV applications (e.g., Cooperative Adaptive Cruise Control) require at least lane-level, or submeter, accuracy.

    This dissertation focuses on the gap between current (affordable) Connected Vehicle positioning technology and CAV applications’ positioning requirements. As a first step, the positioning requirements of a wide range of CAV applications were qualitatively determined. The positioning attributes examined were: required accuracy, type (i.e., relative vs. absolute positioning), and update rate; and the “maximum benefit” accuracy. It was found that lane-level positioning accuracy is critical to enabling functionality in a large number of applications.

    Next, the effect of position uncertainty on application performance was tested in a simulation environment. Two applications—an environmentally-friendly application for signalized intersections, and a safety-focused highway application—were examined. In both cases, it was found that application performance can degrade significantly (or outright fail) when the positioning accuracy is less than required.

    Given the negative effect of position uncertainty on application performance, a position error-tolerant (PE-T) application was developed. A CAV application, (cooperative merging) was developed and modified so that it could function when position errors typical of an urban environment were present. In addition, it was found that adjusting the application in response to position error (by increasing inter-vehicle spacing) could increase application benefits in some scenarios. Another way in which this application is novel compared to most other existing cooperative merging strategies is that it can accommodate application penetration rates varying from 0% to 100%. In fact, significant safety

      Cover page: Integrating Position Uncertainty in Connected and Automated Vehicle ApplicationsCreative Commons 'BY' version 4.0 license
      • Article
      Research Reports (2021)

      As part of the City of Riverside’s Smart-City initiative, UC Riverside researchers have developed an Innovation Corridor testbed for enabling shared electric connected and automated transportation research. This Innovation Corridor testbed is located in Riverside California, and consists of a six-mile section of University Avenue between the UC Riverside campus and downtown Riverside. The testbed supports various transportation modes including passenger vehicles, trucks, transit (e.g., RTA buses), bicycles, and various forms of micro-mobility. This corridor is continuously being instrumented with various infrastructure equipment to support research in shared electric connected and automated transportation. Specifically for this project, the corridor has been equipped with roadside communications equipment and advanced traffic signal controllers at several key intersections, to help improve safety, mobility and environmental sustainability. With this initial instrumentation, we have then conducted connected vehicle experimentation that utilize the signal phase and timing (SPaT) data from these intersections to smooth traffic flow and reduce emissions. For this Innovation Corridor, a high-fidelity simulation environment was also developed to evaluate potential connected vehicle strategies. A variety of Eco-Approach and Departure (EAD) connected vehicle experiments have been conducted and evaluated, both in simulation and in the real-world. As part of the simulation ecosystem, we have compared the energy and emissions modeling results to see which best matches the real-world measurements.

      View the NCST Project Webpage

        Cover page: Development of an Innovation Corridor Testbed for Shared Electric Connected and Automated Transportation
        • Article
        • Peer Reviewed
        2019 Publications (2019)
          Cover page: Evaluating the Environmental Impacts of Connected and Automated Vehicles: Potential Shortcomings of a Binned-Based Emissions Model
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2012)

          Objective

          A major motivation for seeking disease-associated genetic variation is to identify novel risk processes. Although rare copy number variants (CNVs) appear to contribute to attention deficit hyperactivity disorder (ADHD), common risk variants (single-nucleotide polymorphisms [SNPs]) have not yet been detected using genome-wide association studies (GWAS). This raises the concern as to whether future larger-scale, adequately powered GWAS will be worthwhile. The authors undertook a GWAS of ADHD and examined whether associated SNPs, including those below conventional levels of significance, influenced the same biological pathways affected by CNVs.

          Method

          The authors analyzed genome-wide SNP frequencies in 727 children with ADHD and 5,081 comparison subjects. The gene sets that were enriched in a pathway analysis of the GWAS data (the top 5% of SNPs) were tested for an excess of genes spanned by large, rare CNVs in the children with ADHD.

          Results

          No SNP achieved genome-wide significance levels. As previously reported in a subsample of the present study, large, rare CNVs were significantly more common in case subjects than comparison subjects. Thirteen biological pathways enriched for SNP association significantly overlapped with those enriched for rare CNVs. These included cholesterol-related and CNS development pathways. At the level of individual genes, CHRNA7, which encodes a nicotinic receptor subunit previously implicated in neuropsychiatric disorders, was affected by six large duplications in case subjects (none in comparison subjects), and SNPs in the gene had a gene-wide p value of 0.0002 for association in the GWAS.

          Conclusions

          Both common and rare genetic variants appear to be relevant to ADHD and index-shared biological pathways.
            Cover page: Investigating the Contribution of Common Genetic Variants to the Risk and Pathogenesis of ADHD
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2019)
            BACKGROUND: Studies on early-onset presentations of progressive supranuclear palsy (PSP) have been limited to those where a rare monogenic cause has been identified. Here, we have defined early-onset PSP (EOPSP) and investigated its genetic and clinico-pathological profile in comparison with late-onset PSP (LOPSP) and Parkinsons disease (PD). METHODS: We included subjects from the Queen Square Brain Bank, PROSPECT-UK study, and Tracking Parkinsons study. Group comparisons of data were made using Welchs t-test and Kruskal-Wallis analysis of variance. EOPSP was defined as the youngest decile of motor age at onset (≤55 years) in the Queen Square Brain Bank PSP case series. RESULTS: We identified 33 EOPSP, 328 LOPSP, and 2000 PD subjects. The early clinical features of EOPSP usually involve limb parkinsonism and gait freezing, with 50% of cases initially misdiagnosed as having PD. We found that an initial clinical diagnosis of EOPSP had lower diagnostic sensitivity (33%) and positive predictive value (38%) in comparison with LOPSP (80% and 76%) using a postmortem diagnosis of PSP as the gold standard. 3/33 (9%) of the EOPSP group had an underlying monogenic cause. Using a PSP genetic risk score (GRS), we showed that the genetic risk burden in the EOPSP (mean z-score, 0.59) and LOPSP (mean z-score, 0.48) groups was significantly higher (P < 0.05) when compared with the PD group (mean z-score, -0.08). CONCLUSIONS: The initial clinical profile of EOPSP is often PD-like. At the group level, a PSP GRS was able to differentiate EOPSP from PD, and this may be helpful in future diagnostic algorithms. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
              Cover page: The genetic and clinico-pathological profile of early-onset progressive supranuclear palsy.
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2020)
              The 22q11.2 deletion syndrome (22q11DS) is associated with a 20-25% risk of schizophrenia. In a cohort of 962 individuals with 22q11DS, we examined the shared genetic basis between schizophrenia and schizophrenia-related early trajectory phenotypes: sub-threshold symptoms of psychosis, low baseline intellectual functioning and cognitive decline. We studied the association of these phenotypes with two polygenic scores, derived for schizophrenia and intelligence, and evaluated their use for individual risk prediction in 22q11DS. Polygenic scores were not only associated with schizophrenia and baseline intelligence quotient (IQ), respectively, but schizophrenia polygenic score was also significantly associated with cognitive (verbal IQ) decline and nominally associated with sub-threshold psychosis. Furthermore, in comparing the tail-end deciles of the schizophrenia and IQ polygenic score distributions, 33% versus 9% of individuals with 22q11DS had schizophrenia, and 63% versus 24% of individuals had intellectual disability. Collectively, these data show a shared genetic basis for schizophrenia and schizophrenia-related phenotypes and also highlight the future potential of polygenic scores for risk stratification among individuals with highly, but incompletely, penetrant genetic variants.
                Cover page: Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2015)

                Background

                Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts.

                Methods

                We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD).

                Findings

                In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003).

                Interpretation

                Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions.

                Funding

                National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.
                  Cover page: Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2013)
                  OBJECTIVE Although attention deficit hyperactivity disorder (ADHD) is highly heritable, genome-wide association studies (GWAS) have not yet identified any common genetic variants that contribute to risk. There is evidence that aggression or conduct disorder in children with ADHD indexes higher genetic loading and clinical severity. The authors examine whether common genetic variants considered en masse as polygenic scores for ADHD are especially enriched in children with comorbid conduct disorder. METHOD Polygenic scores derived from an ADHD GWAS meta-analysis were calculated in an independent ADHD sample (452 case subjects, 5,081 comparison subjects). Multivariate logistic regression analyses were employed to compare polygenic scores in the ADHD and comparison groups and test for higher scores in ADHD case subjects with comorbid conduct disorder relative to comparison subjects and relative to those without comorbid conduct disorder. Association with symptom scores was tested using linear regression. RESULTS Polygenic risk for ADHD, derived from the meta-analysis, was higher in the independent ADHD group than in the comparison group. Polygenic score was significantly higher in ADHD case subjects with conduct disorder relative to ADHD case subjects without conduct disorder. ADHD polygenic score showed significant association with comorbid conduct disorder symptoms. This relationship was explained by the aggression items. CONCLUSIONS Common genetic variation is relevant to ADHD, especially in individuals with comorbid aggression. The findings suggest that the previously published ADHD GWAS meta-analysis contains weak but true associations with common variants, support for which falls below genome-wide significance levels. The findings also highlight the fact that aggression in ADHD indexes genetic as well as clinical severity.
                    Cover page: High Loading of Polygenic Risk for ADHD in Children With Comorbid Aggression
                    • Article
                    • Peer Reviewed
                    UCLA Previously Published Works (2012)

                    Objective

                    Attention deficit hyperactivity disorder (ADHD) is a common, highly heritable psychiatric disorder. Because of its multifactorial etiology, however, identifying the genes involved has been difficult. The authors followed up on recent findings suggesting that rare copy number variants (CNVs) may be important for ADHD etiology.

                    Method

                    The authors performed a genome-wide analysis of large, rare CNVs (<1% population frequency) in children with ADHD (N=896) and comparison subjects (N=2,455) from the IMAGE II Consortium.

                    Results

                    The authors observed 1,562 individually rare CNVs >100 kb in size, which segregated into 912 independent loci. Overall, the rate of rare CNVs >100 kb was 1.15 times higher in ADHD case subjects relative to comparison subjects, with duplications spanning known genes showing a 1.2-fold enrichment. In accordance with a previous study, rare CNVs >500 kb showed the greatest enrichment (1.28-fold). CNVs identified in ADHD case subjects were significantly enriched for loci implicated in autism and in schizophrenia. Duplications spanning the CHRNA7 gene at chromosome 15q13.3 were associated with ADHD in single-locus analysis. This finding was consistently replicated in an additional 2,242 ADHD case subjects and 8,552 comparison subjects from four independent cohorts from the United Kingdom, the United States, and Canada. Presence of the duplication at 15q13.3 appeared to be associated with comorbid conduct disorder.

                    Conclusions

                    These findings support the enrichment of large, rare CNVs in ADHD and implicate duplications at 15q13.3 as a novel risk factor for ADHD. With a frequency of 0.6% in the populations investigated and a relatively large effect size (odds ratio=2.22, 95% confidence interval=1.5–3.6), this locus could be an important contributor to ADHD etiology.
                      Cover page: Genome-Wide Analysis of Copy Number Variants in Attention Deficit Hyperactivity Disorder: The Role of Rare Variants and Duplications at 15q13.3
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