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Open Access Publications from the University of California

Scholarly Works (13 results)

  • Thesis
  • Peer Reviewed
UC Santa Cruz Electronic Theses and Dissertations (2016)

Software-Defined Networking and Network Function Virtualization lack a unified solution for general network programmability. OpenFlow, Software-Defined Networking’s de-facto standard for network programmability, is based on rule-to-packet header matching, in which an action is executed when a rule involving the packet’s header field is satisfied. However, this model of matching is extremely limited in terms of its expressibility, scalability, and distributability. This is especially evident when satisfying the requirements of a more diverse set of applications. To fully utilize the network as a programmable platform, an architecture that supports an unconstrained, robust computational model is required. A programming platform that can support these attributes must therefore live in software. To make the network fully programable, it is necessary to look at previous Active Networks research as well as current Software Defined Networking research.

We propose that by utilizing the latest virtualization techniques, a new programmable networking architecture is possible and feasible for today’s networks. Such a programmable network architecture would be capable of handling the unique problems of executing code in the network. We call the architecture we have developed to meet these criteria, SCAN.

    Cover page: Analysis of Applicability and Usability of Programmable Networks in Modern NetworksCreative Commons 'BY' version 4.0 license
    • Thesis
    • Peer Reviewed
    UC Santa Cruz Electronic Theses and Dissertations (2016)

    Algorithms for finding the shortest path between two nodes in a graph have been heavily studied for decades. Half a century ago solutions were found for the specific class of problems that involves finding the path of least length, when edges are weighted with a single metric. For example, although the Bellman-Ford and Dijkstra algorithms use different methodologies, both techniques depend on a single metric where the lengths of paths are found by adding path weights in the conventional way. The situation becomes more complicated when multiple metrics or single metrics other than simple Euclidean distance are involved. In these cases, the definition of ``shortest,'' or ``best'' is not straightforward. In addition, it is not always a simple matter to verify that packets in a distributed network are forwarded on best paths that are guaranteed loop-free.

    We call the set of edge weights, plus the rules that determine how paths are concatenated and compared, the path algebra. We find that the structure of a path algebra is the key to understanding how packets will be forwarded in a distributed network; specifically whether they will be forwarded on best, loop-free paths. This is independent of any path-finding algorithm. We show that ``best'' paths and loop-free paths are separate (albeit subtly related) properties, that are dependent on whether or not a path algebra is monotonic and strictly bounded. We examine examples of path algebras that possess various combinations of those two properties in order to illustrate how they affect the construction of forwarding paths. Finally, we prove that if a path algebra is monotonic and strictly bounded, it is guaranteed to produce forwarding paths that are best and loop-free.

    Previous works have introduced these ideas in separate contexts. We provide a consistent framework and prove general concepts that have only been introduced or discussed in special cases.

      Cover page: Herding Packets: How to Route Packets on the Best Path Through a Network with Multiple Criteria
      • Article
      • Peer Reviewed
      UC Santa Cruz Previously Published Works (2022)
        Cover page: QoS Routing Using Dominant-Distance Vectors
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2013)
        Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are relentlessly progressive neurodegenerative disorders with overlapping clinical, genetic and pathological features. Cytoplasmic inclusions of fused in sarcoma (FUS) are the hallmark of several forms of FTLD and ALS patients with mutations in the FUS gene. FUS is a multifunctional, predominantly nuclear, DNA and RNA binding protein. Here, we report that transgenic mice overexpressing wild-type human FUS develop an aggressive phenotype with an early onset tremor followed by progressive hind limb paralysis and death by 12 weeks in homozygous animals. Large motor neurons were lost from the spinal cord accompanied by neurophysiological evidence of denervation and focal muscle atrophy. Surviving motor neurons in the spinal cord had greatly increased cytoplasmic expression of FUS, with globular and skein-like FUS-positive and ubiquitin-negative inclusions associated with astroglial and microglial reactivity. Cytoplasmic FUS inclusions were also detected in the brain of transgenic mice without apparent neuronal loss and little astroglial or microglial activation. Hemizygous FUS overexpressing mice showed no evidence of a motor phenotype or pathology. These findings recapitulate several pathological features seen in human ALS and FTLD patients, and suggest that overexpression of wild-type FUS in vulnerable neurons may be one of the root causes of disease. Furthermore, these mice will provide a new model to study disease mechanism, and test therapies.
          Cover page: Overexpression of human wild-type FUS causes progressive motor neuron degeneration in an age- and dose-dependent fashion
          • Article
          • Peer Reviewed
          UC Santa Cruz Previously Published Works (2014)
          The Cancer Genomics Hub (CGHub) is the online repository of the sequencing programs of the National Cancer Institute (NCI), including The Cancer Genomics Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects, with data from 25 different types of cancer. The CGHub currently contains >1.4 PB of data, has grown at an average rate of 50 TB a month and serves >100 TB per week. The architecture of CGHub is designed to support bulk searching and downloading through a Web-accessible application programming interface, enforce patient genome confidentiality in data storage and transmission and optimize for efficiency in access and transfer. In this article, we describe the design of these three components, present performance results for our transfer protocol, GeneTorrent, and finally report on the growth of the system in terms of data stored and transferred, including estimated limits on the current architecture. Our experienced-based estimates suggest that centralizing storage and computational resources is more efficient than wide distribution across many satellite labs. Database URL: https://cghub.ucsc.edu.
            Cover page: The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data
            • Article
            • Peer Reviewed
            UC Santa Cruz Previously Published Works (2014)
            The Cancer Genomics Hub (CGHub) is the online repository of the sequencing programs of the National Cancer Institute (NCI), including The Cancer Genomics Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects, with data from 25 different types of cancer. The CGHub currently contains >1.4 PB of data, has grown at an average rate of 50 TB a month and serves >100 TB per week. The architecture of CGHub is designed to support bulk searching and downloading through a Web-accessible application programming interface, enforce patient genome confidentiality in data storage and transmission and optimize for efficiency in access and transfer. In this article, we describe the design of these three components, present performance results for our transfer protocol, GeneTorrent, and finally report on the growth of the system in terms of data stored and transferred, including estimated limits on the current architecture. Our experienced-based estimates suggest that centralizing storage and computational resources is more efficient than wide distribution across many satellite labs. Database URL: https://cghub.ucsc.edu.
              Cover page: The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2013)
              Algorithms designed to identify canonical yeast prions predict that around 250 human proteins, including several RNA-binding proteins associated with neurodegenerative disease, harbour a distinctive prion-like domain (PrLD) enriched in uncharged polar amino acids and glycine. PrLDs in RNA-binding proteins are essential for the assembly of ribonucleoprotein granules. However, the interplay between human PrLD function and disease is not understood. Here we define pathogenic mutations in PrLDs of heterogeneous nuclear ribonucleoproteins (hnRNPs) A2B1 and A1 in families with inherited degeneration affecting muscle, brain, motor neuron and bone, and in one case of familial amyotrophic lateral sclerosis. Wild-type hnRNPA2 (the most abundant isoform of hnRNPA2B1) and hnRNPA1 show an intrinsic tendency to assemble into self-seeding fibrils, which is exacerbated by the disease mutations. Indeed, the pathogenic mutations strengthen a 'steric zipper' motif in the PrLD, which accelerates the formation of self-seeding fibrils that cross-seed polymerization of wild-type hnRNP. Notably, the disease mutations promote excess incorporation of hnRNPA2 and hnRNPA1 into stress granules and drive the formation of cytoplasmic inclusions in animal models that recapitulate the human pathology. Thus, dysregulated polymerization caused by a potent mutant steric zipper motif in a PrLD can initiate degenerative disease. Related proteins with PrLDs should therefore be considered candidates for initiating and perhaps propagating proteinopathies of muscle, brain, motor neuron and bone.
                Cover page: Mutations in prion-like domains in hnRNPA2B1 and hnRNPA1 cause multisystem proteinopathy and ALSCreative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC San Francisco Previously Published Works (2016)
                To elucidate the genetic architecture of amyotrophic lateral sclerosis (ALS) and find associated loci, we assembled a custom imputation reference panel from whole-genome-sequenced patients with ALS and matched controls (n = 1,861). Through imputation and mixed-model association analysis in 12,577 cases and 23,475 controls, combined with 2,579 cases and 2,767 controls in an independent replication cohort, we fine-mapped a new risk locus on chromosome 21 and identified C21orf2 as a gene associated with ALS risk. In addition, we identified MOBP and SCFD1 as new associated risk loci. We established evidence of ALS being a complex genetic trait with a polygenic architecture. Furthermore, we estimated the SNP-based heritability at 8.5%, with a distinct and important role for low-frequency variants (frequency 1-10%). This study motivates the interrogation of larger samples with full genome coverage to identify rare causal variants that underpin ALS risk.
                  Cover page: Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis.
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2021)
                  Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.
                    Cover page: Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology
                    • Article
                    • Peer Reviewed
                    UC Irvine Previously Published Works (2018)
                    To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.
                      Cover page: Genome-wide Analyses Identify KIF5A as a Novel ALS Gene.
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