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Open Access Publications from the University of California

Scholarly Works (19 results)

  • Article
  • Peer Reviewed
UC San Francisco Previously Published Works (2014)
Learning comprises multiple components that probably involve cellular and synaptic plasticity at multiple sites. Different neural sites may play their largest roles at different times during behavioral learning. We have used motor learning in smooth pursuit eye movements of monkeys to determine how and when different components of learning occur in a known cerebellar circuit. The earliest learning occurs when one climbing-fiber response to a learning instruction causes simple-spike firing rate of Purkinje cells in the floccular complex of the cerebellum to be depressed transiently at the time of the instruction on the next trial. Trial-over-trial depression and the associated learning in eye movement are forgotten in <6 s, but facilitate long-term behavioral learning over a time scale of ∼5 min. During 100 repetitions of a learning instruction, simple-spike firing rate becomes progressively depressed in Purkinje cells that receive climbing-fiber inputs from the instruction. In Purkinje cells that prefer the opposite direction of pursuit and therefore do not receive climbing-fiber inputs related to the instruction, simple-spike responses undergo potentiation, but more weakly and more slowly. Analysis of the relationship between the learned changes in simple-spike firing and learning in eye velocity suggests an orderly progression of plasticity: first on Purkinje cells with complex-spike (CS) responses to the instruction, later on Purkinje cells with CS responses to the opposite direction of instruction, and last in sites outside the cerebellar cortex. Climbing-fiber inputs appear to play a fast and primary, but nonexclusive, role in pursuit learning.
    Cover page: Role of Plasticity at Different Sites across the Time Course of Cerebellar Motor Learning
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2013)

    The main focus of this thesis is using machine learning and data mining techniques to solve challenging problems. Three problems from different subject areas are discussed: nuclear material detection, drug ranking and target tracking in video sequences. The techniques of the three problems described are all based on an efficiently solvable variant of normalized cut, Normalized Cut Prime (NC').

    The first problem concerns detecting concealed illicit nuclear material, an important part of strategies preventing and deterring nuclear terrorism. What makes this an extremely difficult task are physical limitations of nuclear radiation detectors (arising from energy resolutions and efficiency) and shielding materials terrorists would presumably use to surround the radioactive nuclear material and absorb some of the radiation, thereby reducing the strength of the detected signal. This means the central data analysis problem is identifying a potentially very weak signal, and distinguishing it from both background noise arising from the detector characteristics and naturally occurring environmental radiation. We aim at enhancing the capabilities of detection with algorithmic methods specifically tailored to nuclear data. A novel graph-theory-based methodology based on NC' is used, called Supervised Normalized Cut (SNC). This data mining method classifies measurements obtained from very low resolution plastic scintillation detectors. The accompanying computational study, comparing SNC method with several alternative classification methods shows that in terms of accuracy, the SNC method is on par with alternative approaches, yet SNC is computationally more efficient.

    The second subject area is in the field of drug ranking. This problem refers to placing in rank order, according to their effectiveness, several drugs treating the same disease, using data derived from cell images. Current technologies use the recently developed high-throughput drug profiling (high content screening or HCS). Despite the potential of HCS for accurate descriptions of drug profiles, it produces a deluge of data of quantitative and multidimensional nature, posing analytical challenges in the data mining process. Our new framework is designed to alleviate these difficulties, in the way of producing graph theoretic descriptors and automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS), a way of converting classification to scores. We experimented with the FABS framework by implementing different algorithms and assessing the accuracy of results by a comparative study, which includes other four baseline methods. The conclusion is encouraging: FABS implemented with NC' consistently outperforms other implementations of FABS and alternative methods currently used for ranking that are unrelated to FABS.

    The third problem is target tracking in video sequences -- it can be framed as an unsupervised learning problem: the goal is to delineate a target of interest in a video from background. The tracking task is cast as a graph-cut, incorporating intensity and motion data into the formulation. Tests on real-life benchmark videos show that the developed technique, NC-track, based on NC', is more efficient than many existing techniques, and that it delivers good quality results.

      Cover page: Machine Learning Techniques in Nuclear Material Detection, Drug Ranking and Video Tracking
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2012)

      Motivation

      The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut-normalized cut prime (FABS-NC(')), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.

      Results

      We compare the performance of FABS-NC(') to other methods that could be used for drugs ranking. We devise two variants of the FABS algorithm: FABS-SVM that utilizes support vector machine (SVM) as black box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box. We compare the performance of FABS-NC(') also to three other methods that have been previously considered: center ranking (Center), PCA ranking (PCA), and graph transition energy method (GTEM). The conclusion is encouraging: FABS-NC(') consistently outperforms all these five alternatives. FABS-SVM has the second best performance among these six methods, but is far behind FABS-NC('): In some cases FABS-NC(') produces over half correctly predicted ranking experiment trials than FABS-SVM.

      Availability

      The system and data for the evaluation reported here will be made available upon request to the authors after this manuscript is accepted for publication.
        Cover page: Ranking of multidimensional drug profiling data by fractional-adjusted bi-partitional scores
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2014)
        Serum proteomic analysis can be a valuable approach for the discovery of protein biomarkers for early detection or monitoring of a disease. In this study, two analytical methods were compared for quantification of serum proteins in patients with oral cancer. In the first approach, we quantified serum proteins between oral squamous cell carcinoma (OSCC) and healthy control subjects by performing in-solution digestion of serum proteins, isobaric tags for relative and absolute quantification (iTRAQ) labeling of the resulting peptides, strong cation exchange (SCX) fractionation of labeled peptides and finally capillary liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis of the peptides. In the second approach, we first separated serum proteins with SDS-PAGE. The gel-separated proteins were then digested with trypsin and the resulting peptides were labeled with iTRAQ and analyzed with LC-MS/MS for protein quantification. A total of 319 serum proteins were quantified with the first proteomic approach whereas a total of 281 proteins were quantified by the second proteomic approach. Most of the proteins were identified and quantified by both approaches, suggesting that these methods are similarly effective for serum proteome analysis. This study provides compelling evidence that quantitative serum proteomic analysis of OSCC is a valuable approach for identifying differentially expressed proteins in cancer patients' circulation systems that may be used as potential biomarkers for disease detection. Further validation in large oral cancer patient populations may lead to a simple and low invasive clinical tool for OSCC diagnosis or monitoring.
          Cover page: Quantitative Proteomic Analysis of Serum Proteins from Oral Cancer Patients: Comparison of Two Analytical Methods
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2016)
          Metastasis represents a key factor associated with poor prognosis of head and neck squamous cell carcinoma (HNSC). However, the underlying molecular mechanisms remain largely unknown. In this study, our liquid chromatography with tandem mass spectrometry analysis revealed a number of significantly differentially expressed membrane/membrane-associated proteins between high invasive UM1 and low invasive UM2 cells. One of the identified membrane proteins, Syntenin-1, was remarkably up-regulated in HNSC tissues and cell lines when compared to the controls, and also over-expressed in recurrent HNSC and high invasive UM1 cells. Syntenin-1 over-expression was found to be significantly associated with lymph node metastasis and disease recurrence. HNSC patients with higher syntenin-1 expression had significantly poorer long term overall survival and similar results were found in many other types of cancers based on analysis of The Cancer Genome Atlas data. Finally, knockdown of syntenin-1 inhibited the proliferation, migration and invasion of HNSC cells, and opposite findings were observed when syntenin-1 was over-expressed. Collectively, our studies indicate that syntenin-1 promotes invasion and progression of HNSC. It may serve as a valuable biomarker for lymph node metastasis or a potential target for therapeutic intervention in HNSC.
            Cover page: Syntenin-1 is a promoter and prognostic marker of head and neck squamous cell carcinoma invasion and metastasis
            • Article
            • Peer Reviewed
            LBL Publications (2018)
            The sulfur induced embrittlement of polycrystalline nickel (Ni) metal has been a long-standing mystery. It is suggested that sulfur impurity makes ductile Ni metal brittle in many industry applications due to various mechanisms, such as impurity segregation and disorder-induced melting etc. Here we report an observation that the most ductile measurement occurs at a critical sulfur doping concentration, 14 at.% at pressure from 14 GPa up to 29 GPa through texture evolution analysis. The synchrotron-based high pressure texturing measurements using radial diamond anvil cell (rDAC) X-ray diffraction (XRD) techniques reveal that the activities of slip systems in the polycrystalline nickel metal are affected by sulfur impurities and external pressures, giving rise to the changes in the plastic deformation of the nickel metal. Dislocation dynamics (DD) simulation on dislocation density and velocity further confirms the pressure induced ductilization changes in S doped Ni metal. This observation and simulation suggests that the ductilization of the doped polycrystalline nickel metal can be optimized by engineering the sulfur concentration under pressure, shedding a light on tuning the mechanical properties of this material for better high pressure applications.
              • Article
              • Peer Reviewed
              LBL Publications (2021)
              The excellent mechanical properties of high-entropy alloys (HEAs) make them promising materials for advances in science and technology. However, the underlying mechanism of plastic deformation is not well understood. In situ experiments are urgently required to provide a fundamental understanding of the plastic deformation under high pressure. We performed in situ synchrotron X-ray diffraction (XRD) experiments to study compression deformation behavior of the HEA MoNbTaVW in a radial diamond anvil cell (rDAC). Our results show that the strength and ratio of the stress-to-shear modulus values are ~1.5 and 3 times that of pure tungsten (W), respectively. MoNbTaVW showed plastic deformation above 5 GPa and displayed a much stronger texture. We found that the active dislocation behavior is mainly responsible for the high strength in MoNbTaVW under compression. This unique technique opens a new avenue to investigate the in situ mechanical properties and their mechanism in other types of HEAs.
                Cover page: In situ study on the compression deformation of MoNbTaVW high-entropy alloy
                • Article
                • Peer Reviewed
                Berkeley Research Impact Initiative (BRII) (2011)
                Despite the great progress made towards smoke-free environments, only 9% of countries worldwide mandate smoke-free restaurants and bars. Smoking was generally not regulated in restaurants and bars in China before 2008. This study was designed to examine the public attitudes towards banning smoking in these places in China. A convenience sample of 814 restaurants and bars was selected in five Chinese cities and all owners of these venues were interviewed in person by questionnaire in 2007. Eighty six percent of current nonsmoking subjects had at least one-day exposure to secondhand smoke (SHS) at work in the past week. Only 51% of subjects knew SHS could cause heart disease. Only 17% and 11% of subjects supported prohibiting smoking completely in restaurants and in bars, respectively, while their support for restricting smoking to designated areas was much higher. Fifty three percent of subjects were willing to prohibit or restrict smoking in their own venues. Of those unwilling to do so, 82% thought smoking bans would reduce revenue, and 63% thought indoor air quality depended on ventilation rather than smoking bans. These results showed that there was support for smoking bans among restaurant or bar owners in China despite some knowledge gaps. To facilitate smoking bans in restaurants and bars, it is important to promote health education on specific hazards of SHS, provide country-specific evidence on smoking bans and hospitality revenues, and disseminate information that restricting smoking and ventilation alone cannot eliminate SHS hazards.
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2022)
                  The controlled functionalization of a single fluorine in a CF3 group is difficult and rare. Photochemical C-F bond functionalization of the sp3-C-H bond in trifluorotoluene, PhCF3, is achieved using catalysts made from earth-abundant lanthanides, (CpMe4)2Ln(2-O-3,5- t Bu2-C6H2)(1-C{N(CH)2N(iPr)}) (Ln = La, Ce, Nd and Sm, CpMe4 = C5Me4H). The Ce complex is the most effective at mediating hydrodefluorination and defluoroalkylative coupling of PhCF3 with alkenes; addition of magnesium dialkyls enables catalytic C-F bond cleavage and C-C bond formation by all the complexes. Mechanistic experiments confirm the essential role of the Lewis acidic metal and support an inner-sphere mechanism of C-F activation. Computational studies agree that coordination of the C-F substrate is essential for C-F bond cleavage. The unexpected catalytic activity for all members is made possible by the light-absorbing ability of the redox non-innocent ligands. The results described herein underscore the importance of metal-ligand cooperativity, specifically the synergy between the metal and ligand in both light absorption and redox reactivity, in organometallic photocatalysis.
                    Cover page: Controlled monodefluorination and alkylation of C(sp 3 )–F bonds by lanthanide photocatalysts: importance of metal–ligand cooperativity
                    • Article
                    • Peer Reviewed
                    UCLA Previously Published Works (2022)

                    Objective

                    Chronic pancreatitis (CP) is a potentially fatal disease of the exocrine pancreas, with no specific or effective approved therapies. Due to difficulty in accessing pancreas tissues, little is known about local immune responses or pathogenesis in human CP. We sought to characterise pancreatic immune responses using tissues derived from patients with different aetiologies of CP and non-CP organ donors in order to identify key signalling molecules associated with human CP.

                    Design

                    We performed single-cell level cellular indexing of transcriptomes and epitopes by sequencing and T-cell receptor (TCR) sequencing of pancreatic immune cells isolated from organ donors, hereditary and idiopathic patients with CP who underwent total pancreatectomy. We validated gene expression data by performing flow cytometry and functional assays in a second patient with CP cohort.

                    Results

                    Deep single-cell sequencing revealed distinct immune characteristics and significantly enriched CCR6+ CD4+ T cells in hereditary compared with idiopathic CP. In hereditary CP, a reduction in T-cell clonality was observed due to the increased CD4+ T (Th) cells that replaced tissue-resident CD8+ T cells. Shared TCR clonotype analysis among T-cell lineages also unveiled unique interactions between CCR6+ Th and Th1 subsets, and TCR clustering analysis showed unique common antigen binding motifs in hereditary CP. In addition, we observed a significant upregulation of the CCR6 ligand (CCL20) expression among monocytes in hereditary CP as compared with those in idiopathic CP. The functional significance of CCR6 expression in CD4+ T cells was confirmed by flow cytometry and chemotaxis assay.

                    Conclusion

                    Single-cell sequencing with pancreatic immune cells in human CP highlights pancreas-specific immune crosstalk through the CCR6-CCL20 axis, a signalling pathway that might be leveraged as a potential future target in human hereditary CP.
                      Cover page: Single-cell sequencing unveils distinct immune microenvironments with CCR6-CCL20 crosstalk in human chronic pancreatitis
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