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Open Access Publications from the University of California

Scholarly Works (93 results)

  • Article
  • Peer Reviewed
Papers (2007)

This paper introduces an adaptive sampling algorithm for a mobile sensor network to estimate a scalar field. The sensor network consists of static nodes and one mobile robot. The static nodes are able to take sensor readings continuously in place, while the mobile robot is able to move and sample at multiple locations. The measurements from the robot and the static nodes are used to reconstruct an underlying scalar field. The algorithm presented in this paper accepts the measurements made by the static nodes as inputs and computes a path for the mobile robot which minimizes the integrated mean square error of the reconstructed field subject to the constraint that the robot has limited energy. We assume that the field does not change when robot is taking samples. In addition to simulations, we have validated the algorithm on a robotic boat and a system of static buoys operating in a lake over several km of traversed distance while reconstructing the temperature field of the lake surface.

    Cover page: Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2022)
    Many membraneless organelles, or biological condensates, form through phase separation, and play key roles in signal sensing and transcriptional regulation. While the functional importance of these condensates has inspired many studies to characterize their stability and spatial organization, the underlying principles that dictate these emergent properties are still being uncovered. In this review, we examine recent work on biological condensates, especially multicomponent systems. We focus on connecting molecular factors such as binding energy, valency, and stoichiometry with the interfacial tension, explaining the nontrivial interior organization in many condensates. We further discuss mechanisms that arrest condensate coalescence by lowering the surface tension or introducing kinetic barriers to stabilize the multidroplet state.
      Cover page: Molecular Determinants for the Layering and Coarsening of Biological Condensates.Creative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      Papers (2004)

      We describe the design and construction of an underwater sensor actuator network to detect extreme temperature gradients. We are motivated by the fact that regions of sharp temperature change (thermoclines) are a breeding ground for certain marine microorganisms. We present a distributed algorithm using local communication based on binary search to find a thermocline by using a mobile sensor network. Simulations and experiments using a mote test bed demonstrate the validity of this approach. We also discuss the improvement in energy efficiency using a submarine robot as a data mule. Comparisons between experimental data with and without the data mule show that there are considerable energy savings in the sensor network due to the data mule.

        Cover page: Adaptive Sampling for Marine Microorganism Monitoring
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2023)
        Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performance on a single GPU that rivals the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that OpenABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates.
          Cover page: OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates.Creative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2024)
          Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by nonspecific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity toward specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.
            Cover page: Nonspecific Yet Selective Interactions Contribute to Small Molecule Condensate Binding.
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2016)
            It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.
              Cover page: Molecular and genetic inflammation networks in major human diseases
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2019)
              Unlike nanostructured metal catalysts, supported single-atom catalysts (SACs) contain only atomically dispersed metal atoms, hinting at much more pronounced metal-support effects. Herein, we take a series of polyoxometalate-supported Pt catalysts as examples to quantitatively investigate the stability of Pt atoms on oxide supports and how the Pt-support interaction influences the catalytic performance. For this entire series, we show that the Pt atoms prefer to stay at a 4-fold hollow site of one polyoxometalate molecule and that the least adsorption energy to obtain sintering-resistant Pt SACs is 5.50 eV, which exactly matches the cohesive energy of bulk Pt metal. Further, we compared their catalytic performance in several hydrogenation reactions and simulated the reaction pathways of propene hydrogenation by density functional theory (DFT) calculations. Both experimental and theoretical approaches suggest that despite the Pt1-support interactions being different, the reaction pathways of various Pt1-polyoxometalate catalysts are very similar and their effective reaction barriers are close to each other and as low as 24 kJ/mol, indicating the possibility of obtaining SACs with improved stability without compromising activity. DFT calculations show that all reaction elementary steps take place only on the Pt atom without involving neighboring O atoms and that hydrogenation proceeds from the molecularly adsorbed H2 species. Pt SACs give a weaker H2 adsorption energy than Pt clusters or surfaces, resulting in small adsorption equilibrium constants and small apparent activation barriers, which agree between experiment and theory.
              • 1 supplemental PDF
              Cover page: Atomically Dispersed Pt1–Polyoxometalate Catalysts: How Does Metal–Support Interaction Affect Stability and Hydrogenation Activity?
              • Article
              • Peer Reviewed
              UC San Francisco Previously Published Works (2020)
              Variations in craniofacial morphology may arise as a result of adaptation to different environmental factors such as soft diet (SD), which lessens functional masticatory load. Prior studies have shown that changes in the masticatory muscle function associated with a switch to short-term SD led to changes in craniofacial morphology and alveolar bone architecture. However, the long-term effects of SD and the associated adaptive changes in craniofacial shape are unclear. Our novel study set out to profile prospective skull changes in mice fed with SDs over multiple generations using three-dimensional (3D) geometric morphometric analysis (GMA). Our results revealed that short-term SD consumption led to a significant decrease in craniofacial size, along with numerous shape changes. Long-term SD consumption over 15 continuous generations was not associated with changes in craniofacial size; however, shape analysis revealed mice with shortened crania and mandibles in the anteroposterior dimension, as well as relative widening in the transverse dimension compared to the average shape of all mice analyzed in our study. Moreover, changes in shape and size associated with different functional loads appeared to be independent - shape changes persisted after diets were switched for one generation, whereas size decreased after one generation and then returned to baseline size. Our study is the first to study the role of prolonged, multi-generational SD consumption in the determination of craniofacial size and shape.
                Cover page: Effects of Multi-Generational Soft Diet Consumption on Mouse Craniofacial MorphologyCreative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC San Francisco Previously Published Works (2018)
                The aim of this study was to evaluate the role of epithelial signal transducer and activator of transcription 3 (STAT3) in mouse incisor amelogenesis. Since Stat3 is expressed in the epithelial component of developing and adult mouse teeth, we generated and analyzed Krt14Cre/+;Stat3fl/fl mutant mice in which Stat3 was inactivated in epithelia including ameloblast progenitors and ameloblasts, the cells responsible for enamel formation. Histological analysis showed little enamel matrix in mutant incisors compared to controls. Delayed incisor enamel mineralization was demonstrated using micro-computed X-ray tomography analysis and was supported by an increase in the pre-expression distance of enamel-enriched proteins such as amelogenin, ameloblastin, and kallikrein-4. Lastly, scanning electron microscopy analysis showed little enamel mineralization in mutant incisors underneath the mesial root of the 1st molar; however, the micro-architecture of enamel mineralization was similar in the erupted portion of control and mutant incisors. Taken together, our findings demonstrate for the first time that the absence of epithelial Stat3 in mice leads to delayed incisor amelogenesis.
                  Cover page: The Role of Epithelial Stat3 in Amelogenesis during Mouse Incisor Renewal
                  • Article
                  Papers (2007)

                  This paper describes the design, development, and initial application of a sensor-actuated network for sensing and sampling microbial communities in aquatic ecosystems. The network consists of ten stationary buoys and one mobile robotic boat for real-time, in situ measurements and analysis of chemical and physical factors governing the abundance and dynamics of microorganisms at biologically relevant spatiotemporal scales. The goal of the network is to obtain high-resolution information on the spatial and temporal distributions of plankton assemblages and concomitant environmental parameters in aquatic environments using the in situ presence afforded by the network and to make possible network-enabled robotic sampling of hydrographic features of interest. This work constitutes advances in (1) real-time observing in aquatic ecosystems and (2) sensor actuated sampling for biological analysis.

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