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Open Access Publications from the University of California

Scholarly Works (214 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2015)

This dissertation aims to improve ISO operations through a better real-time market solution algorithm that directly considers both real and reactive power, finds a feasible Alternating Current Optimal Power Flow solution, and allows for solving transmission switching problems in an AC setting. We show how to enhance the current IEEE data sets by adding appropriate thermal limits and how to convert apparent power limits to current limits, as current limits are closer to the 'actual' physical limit. We introduce a sequential linearized programming (SLP) approximation of the current-voltage formulation of the ACOPF. We show how the SLP approximation yields AC feasible generator dispatch profiles with little error in cost compared to the exact problem and that the SLP computational time is linear in the problem size. Next, we show how the SLP can be used to run a more complete real-time power market. We show how to use the SLP formulation in corrective switching and how it fixes issues with voltage and line flow. We also test out common heuristics for economic switching.

Most of the IEEE systems do not contain any thermal limits on lines, and the ones that do are often not binding. Chapter 3 modifies the thermal limits for the IEEE systems to create new, interesting test cases. Algorithms created to better solve the power flow problem often solve the IEEE cases without line limits. However, one of the factors that makes the power flow problem hard is thermal limits on the lines. The transmission networks in practice often have transmission lines that become congested, and it is unrealistic to ignore line limits. Modifying the IEEE test cases makes it possible for other researchers to be able to test their algorithms on a setup that is closer to the actual ISO setup. This thesis also examines how to convert limits given on apparent power - as is in the case in the Polish test systems - to limits on current. The main consideration in setting line limits is temperature, which linearly relates to current. Setting limits on real or apparent power is actually a proxy for using the limits on current. Therefore, Chapter 3 shows how to convert back to the best physical representation of line limits.

A sequential linearization of the current-voltage formulation of the Alternating Current Optimal Power Flow (ACOPF) problem is used to find an AC-feasible generator dispatch. In this sequential linearization, there are parameters that are set to the previous optimal solution. Additionally, to improve accuracy of the Taylor series approximations that are used, the movement of the voltage is restricted. The movement of the voltage is allowed to be very large at the first iteration and is restricted further on each subsequent iteration, with the restriction corresponding to the accuracy and AC-feasiblity of the solution. This linearization was tested on the IEEE and Polish systems, which range from 14 to 3375 buses and 20 to 4161 transmission lines. It had an accuracy of 0.5% or less for all but the 30-bus system. It also solved in linear time with CPLEX, while the non-linear version solved in $O(n^{1.11})$ to $O(n^{1.39})$. The sequential linearization is slower than the nonlinear formulation for smaller problems, but faster for larger problems, and its linear computational time means it would continue solving faster for larger problems.

A major consideration to implementing algorithms to solve the optimal generator dispatch is ensuring that the resulting prices from the algorithm will support the market. Since the sequential linearization is linear, it is convex, its marginal values are well-defined, and there is no duality gap. The prices and settlements obtained from the sequential linearization therefore can be used to run a market. This market will include extra prices and settlements for reactive power and voltage, compared to the present-day market, which is based on real power. An advantage of this is that there is a very clear pool that can be used for reactive power/voltage support payments, while presently there is not a clear pool to take them out of. This method also reveals how valuable reactive power and voltage are at different locations, which can enable better planning of reactive resource construction.

Transmission switching increases the feasible region of the generator dispatch, which means there may be a better solution than without transmission switching. Power flows on transmission lines are not directly controllable; rather, the power flows according to how it is injected and the physical characteristics of the lines. Changing the network topology changes the physical characteristics, which changes the flows. This means that sets of generator dispatch that may have previously been infeasible due to the flow exceeding line constraints may be feasible, since the flows will be different and may meet line constraints. However, transmission switching is a mixed integer problem, which may have a very slow solution time. For economic switching, we examine a series of heuristics. We examine the congestion rent heuristic in detail and then examine many other heuristics at a higher level. Post-contingency corrective switching aims to fix issues in the power network after a line or generator outage. In Chapter 7, we show that using the sequential linear program with corrective switching helps solve voltage and excessive flow issues.

    Cover page: Improving the Energy Market: Algorithms, Market Implications, and Transmission Switching
    • Article
    • Peer Reviewed
    UC Davis Previously Published Works (2022)
    Many important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K) to connect genotype to phenotype. Genomic relationship matrices share information among entries to estimate the observed entries' genetic values and predict unobserved entries' genetic values. One of the main parameters of such models is genomic variance (σg2), or the variance of a trait associated with a genome-wide sample of DNA polymorphisms, and genomic heritability (hg2); however, the seminal papers introducing different forms of K often do not discuss their effects on the model estimated variance components despite their importance in genetic research and breeding. Here, we discuss the effect of several standard methods for calculating the genomic relationship matrix on estimates of σg2 and hg2. With current approaches, we found that the genomic variance tends to be either overestimated or underestimated depending on the scaling and centering applied to the marker matrix (Z), the value of the average diagonal element of K, and the assortment of alleles and heterozygosity (H) in the observed population. Using the average semivariance, we propose a new matrix, KASV, that directly yields accurate estimates of σg2 and hg2 in the observed population and produces best linear unbiased predictors equivalent to routine methods in plants and animals.
      Cover page: Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses.
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2016)

      Background

      Genome-wide association study (GWAS) is a useful tool for detecting and characterizing traits of interest including those associated with disease resistance in soybean. The availability of 50,000 single nucleotide polymorphism (SNP) markers (SoySNP50K iSelect BeadChip; www.soybase.org ) on 19,652 soybean and wild soybean plant introductions (PIs) in the USDA Soybean Germplasm Collection allows for fast and robust identification of loci associated with a desired phenotype. By using a genome-wide marker set to predict phenotypic values, genomic prediction for phenotype-unknown but genotype-determined PIs has become possible. The goal of this study was to describe the genetic architecture associated with sensitivity to Tobacco ringspot virus (TRSV) infection in the USDA Soybean Germplasm Collection.

      Results

      TRSV-induced disease sensitivities of the 697 soybean PIs were rated on a one to five scale with plants rated as one exhibiting mild symptoms and plants rated as five displaying terminal bud necrosis (i.e., bud blight). The GWAS identified a single locus on soybean chromosome 2 strongly associated with TRSV sensitivity. Cross-validation showed a correlation of 0.55 (P < 0.01) to TRSV sensitivity without including the most significant SNP marker from the GWAS as a covariate, which was a better estimation compared to the mean separation by using significant SNPs. The genomic estimated breeding values for the remaining 18,955 unscreened soybean PIs in the USDA Soybean Germplasm Collection were obtained using the GAPIT R package. To evaluate the prediction accuracy, an additional 55 soybean accessions were evaluated for sensitivity to TRSV, which resulted in a correlation of 0.67 (P < 0.01) between actual and predicted severities.

      Conclusion

      A single locus responsible for TRSV sensitivity in soybean was identified on chromosome 2. Two leucine-rich repeat receptor-like kinase genes were located near the locus and may control sensitivity of soybean to TRSV infection. Furthermore, a comprehensive genomic prediction for TRSV sensitivity for all accessions in the USDA Soybean Germplasm Collection was completed.
        Cover page: Genome-wide association and genomic prediction identifies associated loci and predicts the sensitivity of Tobacco ringspot virus in soybean plant introductions
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2015)
        Plants produce a wide variety of defensive metabolites to protect themselves against herbivores and pathogens. Non-protein amino acids, which are present in many plant species, can have a defensive function through their mis-incorporation during protein synthesis and/or inhibition of biosynthetic pathways in primary metabolism. 5-Hydroxynorvaline was identified in a targeted search for previously unknown non-protein amino acids in the leaves of maize (Zea mays) inbred line B73. Accumulation of this compound increases during herbivory by aphids (Rhopalosiphum maidis, corn leaf aphid) and caterpillars (Spodoptera exigua, beet armyworm), as well as in response to treatment with the plant signalling molecules methyl jasmonate, salicylic acid and abscisic acid. In contrast, ethylene signalling reduced 5-hydroxynorvaline abundance. Drought stress induced 5-hydroxynorvaline accumulation to a higher level than insect feeding or treatment with defence signalling molecules. In field-grown plants, the 5-hydroxynorvaline concentration was highest in above-ground vegetative tissue, but it was also detectable in roots and dry seeds. When 5-hydroxynorvaline was added to aphid artificial diet at concentrations similar to those found in maize leaves and stems, R. maidis reproduction was reduced, indicating that this maize metabolite may have a defensive function. Among 27 tested maize inbred lines there was a greater than 10-fold range in the accumulation of foliar 5-hydroxynorvaline. Genetic mapping populations derived from a subset of these inbred lines were used to map quantitative trait loci for 5-hydroxynorvaline accumulation to maize chromosomes 5 and 7.
          Cover page: Accumulation of 5-hydroxynorvaline in maize (Zea mays) leaves is induced by insect feeding and abiotic stress
          • Article
          • Peer Reviewed
          UC Davis Previously Published Works (2020)
          Herbicide application is crucial for weed management in most crop production systems, but for sorghum herbicide options are limited. Sorghum is sensitive to residual protoporphyrinogen oxidase (PPO)-inhibiting herbicides, such as fomesafen, and a long re-entry period is required before sorghum can be planted after its application. Improving sorghum for tolerance to such residual herbicides would allow for increased sorghum production and the expansion of herbicide options for growers. In this study, we observed sorghum tolerance to residual fomesafen. To investigate the underlying tolerance mechanism a genome-wide association mapping study was conducted using field-collected sorghum biomass panel (SBP) data, and a greenhouse assay was developed to confirm the field phenotypes. A total of 26 significant SNPs (FDR<0.05), spanning a 215.3 kb region on chromosome 3, were detected. The ten most significant SNPs included two in genic regions (Sobic.003G136800, and Sobic.003G136900) and eight SNPs in the intergenic region encompassing the genes Sobic.003G136700, Sobic.003G136800, Sobic.003G137000, Sobic.003G136900, and Sobic.003G137100. The gene Sobic.003G137100 (PPXI), which encodes the PPO1 enzyme, one of the targets of PPO-inhibiting herbicides, was located 12kb downstream of the significant SNP S03_13152838. We found that PPXI is highly conserved in sorghum and expression does not significantly differ between tolerant and sensitive sorghum lines. Our results suggest that PPXI most likely does not underlie the observed herbicide tolerance. Instead, the mechanism underlying herbicide tolerance in the SBP is likely metabolism-based resistance, possibly regulated by the action of multiple genes. Further research is necessary to confirm candidate genes and their functions.
            Cover page: Genetic variation associated with PPO-inhibiting herbicide tolerance in sorghum
            • Article
            • Peer Reviewed
            UC Davis Previously Published Works (2020)
            Plant growth, development, and nutritional quality depends upon amino acid homeostasis, especially in seeds. However, our understanding of the underlying genetics influencing amino acid content and composition remains limited, with only a few candidate genes and quantitative trait loci identified to date. Improved knowledge of the genetics and biological processes that determine amino acid levels will enable researchers to use this information for plant breeding and biological discovery. Toward this goal, we used genomic prediction to identify biological processes that are associated with, and therefore potentially influence, free amino acid (FAA) composition in seeds of the model plant Arabidopsis thaliana Markers were split into categories based on metabolic pathway annotations and fit using a genomic partitioning model to evaluate the influence of each pathway on heritability explained, model fit, and predictive ability. Selected pathways included processes known to influence FAA composition, albeit to an unknown degree, and spanned four categories: amino acid, core, specialized, and protein metabolism. Using this approach, we identified associations for pathways containing known variants for FAA traits, in addition to finding new trait-pathway associations. Markers related to amino acid metabolism, which are directly involved in FAA regulation, improved predictive ability for branched chain amino acids and histidine. The use of genomic partitioning also revealed patterns across biochemical families, in which serine-derived FAAs were associated with protein related annotations and aromatic FAAs were associated with specialized metabolic pathways. Taken together, these findings provide evidence that genomic partitioning is a viable strategy to uncover the relative contributions of biological processes to FAA traits in seeds, offering a promising framework to guide hypothesis testing and narrow the search space for candidate genes.
              Cover page: Genomic Prediction Informed by Biological Processes Expands Our Understanding of the Genetic Architecture Underlying Free Amino Acid Traits in Dry <i>Arabidopsis</i> Seeds.
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2017)
              To ensure food security, maize (Zea mays) is a model crop for understanding useful traits underlying stress resistance. In contrast to foliar biochemicals, root defenses limiting the spread of disease remain poorly described. To better understand belowground defenses in the field, we performed root metabolomic profiling and uncovered unexpectedly high levels of the sesquiterpene volatile β-selinene and the corresponding nonvolatile antibiotic derivative β-costic acid. The application of metabolite-based quantitative trait locus mapping using biparental populations, genome-wide association studies, and near-isogenic lines enabled the identification of terpene synthase21 (ZmTps21) on chromosome 9 as a β-costic acid pathway candidate gene. Numerous closely examined β-costic acid-deficient inbred lines were found to harbor Zmtps21 pseudogenes lacking conserved motifs required for farnesyl diphosphate cyclase activity. For biochemical validation, a full-length ZmTps21 was cloned, heterologously expressed in Escherichia coli, and demonstrated to cyclize farnesyl diphosphate, yielding β-selinene as the dominant product. Consistent with microbial defense pathways, ZmTps21 transcripts strongly accumulate following fungal elicitation. Challenged field roots containing functional ZmTps21 alleles displayed β-costic acid levels over 100 μg g-1 fresh weight, greatly exceeding in vitro concentrations required to inhibit the growth of five different fungal pathogens and rootworm larvae (Diabrotica balteata). In vivo disease resistance assays, using ZmTps21 and Zmtps21 near-isogenic lines, further support the endogenous antifungal role of selinene-derived metabolites. Involved in the biosynthesis of nonvolatile antibiotics, ZmTps21 exists as a useful gene for germplasm improvement programs targeting optimized biotic stress resistance.
                Cover page: Selinene Volatiles Are Essential Precursors for Maize Defense Promoting Fungal Pathogen Resistance
                • Article
                • Peer Reviewed
                UC San Francisco Previously Published Works (2017)
                  Cover page: GENOME WIDE DNA METHYLATION PROFILING IS PREDICTIVE OF OUTCOME IN JUVENILE MYELOMONOCYTIC LEUKEMIA
                  • Article
                  • Peer Reviewed
                  UC Davis Previously Published Works (2014)
                  Efforts are underway for development of crops with improved levels of provitamin A carotenoids to help combat dietary vitamin A deficiency. As a global staple crop with considerable variation in kernel carotenoid composition, maize (Zea mays L.) could have a widespread impact. We performed a genome-wide association study (GWAS) of quantified seed carotenoids across a panel of maize inbreds ranging from light yellow to dark orange in grain color to identify some of the key genes controlling maize grain carotenoid composition. Significant associations at the genome-wide level were detected within the coding regions of zep1 and lut1, carotenoid biosynthetic genes not previously shown to impact grain carotenoid composition in association studies, as well as within previously associated lcyE and crtRB1 genes. We leveraged existing biochemical and genomic information to identify 58 a priori candidate genes relevant to the biosynthesis and retention of carotenoids in maize to test in a pathway-level analysis. This revealed dxs2 and lut5, genes not previously associated with kernel carotenoids. In genomic prediction models, use of markers that targeted a small set of quantitative trait loci associated with carotenoid levels in prior linkage studies were as effective as genome-wide markers for predicting carotenoid traits. Based on GWAS, pathway-level analysis, and genomic prediction studies, we outline a flexible strategy involving use of a small number of genes that can be selected for rapid conversion of elite white grain germplasm, with minimal amounts of carotenoids, to orange grain versions containing high levels of provitamin A.
                    Cover page: A Foundation for Provitamin A Biofortification of Maize: Genome-Wide Association and Genomic Prediction Models of Carotenoid Levels
                    • Article
                    • Peer Reviewed
                    UC Davis Previously Published Works (2020)

                    Background

                    The functional genome of agronomically important plant species remains largely unexplored, yet presents a virtually untapped resource for targeted crop improvement. Functional elements of regulatory DNA revealed through profiles of chromatin accessibility can be harnessed for fine-tuning gene expression to optimal phenotypes in specific environments.

                    Result

                    Here, we investigate the non-coding regulatory space in the maize (Zea mays) genome during early reproductive development of pollen- and grain-bearing inflorescences. Using an assay for differential sensitivity of chromatin to micrococcal nuclease (MNase) digestion, we profile accessible chromatin and nucleosome occupancy in these largely undifferentiated tissues and classify at least 1.6% of the genome as accessible, with the majority of MNase hypersensitive sites marking proximal promoters, but also 3' ends of maize genes. This approach maps regulatory elements to footprint-level resolution. Integration of complementary transcriptome profiles and transcription factor occupancy data are used to annotate regulatory factors, such as combinatorial transcription factor binding motifs and long non-coding RNAs, that potentially contribute to organogenesis, including tissue-specific regulation between male and female inflorescence structures. Finally, genome-wide association studies for inflorescence architecture traits based solely on functional regions delineated by MNase hypersensitivity reveals new SNP-trait associations in known regulators of inflorescence development as well as new candidates.

                    Conclusions

                    These analyses provide a comprehensive look into the cis-regulatory landscape during inflorescence differentiation in a major cereal crop, which ultimately shapes architecture and influences yield potential.
                      Cover page: The regulatory landscape of early maize inflorescence development
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