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Open Access Publications from the University of California

Scholarly Works (53 results)

  • Article
  • Peer Reviewed
UC Riverside Previously Published Works (2015)
    Cover page: Understanding Dynamic Social Grouping Behaviors of Pedestrians
    • Thesis
    • Peer Reviewed
    UC Irvine Electronic Theses and Dissertations (2017)

    Cancer metastases are responsible for more than 90% of cancer deaths, however no current effective therapeutics directly and specifically targets them. The exclusive mechanical properties of metastatic niche offer an intriguing target for the development of treatments selectively targeting metastases. Systemically infused mesenchymal stem cells (MSCs) preferentially home to tumors. Besides, it has been established that tissue mechanical properties regulate MSC differentiation by driving expression of certain genes. We hypothesize that increased matrix stiffness is an essential property of the metastatic niche that can be targeted with MSC-based, mechano-responsive therapies. Here we presented, by targeting the mechano-environment of the metastatic niche, a new methodology for the treatment of cancer metastases, using promoter-driven, MSC-based vectors, named as mechano-responsive cell system (MRCS). Our data suggest that the MRCS homes to and targets cancer metastasis responding to specific mechanical microenvironment to deliver therapeutics, such as cytosine deaminase (CD) that locally activates the prodrug to kill cancer with minimal side-effects. Compared to MSCs expressing CD constitutively, MRCS not only treats metastatic breast cancer with reduced deleterious effects and more effective outcome, but may also serve as a platform technology for prospective application to therapies targeting abnormal tissue stiffness including fibrotic diseases.

      Cover page: Mechano-Responsive Stem Cells for Cancer Metastasis Diagnostics and Therapeutics Through Biophysical Cues
      • Article
      • Peer Reviewed
      UC Riverside Previously Published Works (2016)
      Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches.
        Cover page: Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval
        • Thesis
        • Peer Reviewed
        UC Riverside Electronic Theses and Dissertations (2014)

        Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined high level image semantic representation. Through experiments in applications including automatic image annotation, semantic image retrieval, moth species identification and multi-pedestrian tracking on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the proposed image semantic representation in comparison with state-of-the-art approaches.

          Cover page: Exploring Semantic Concept Co-Occurrences for Image Based Applications
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2018)

          Probing the biophysical properties of the tumor niche offers a new perspective in cancer mechanobiology, and supports the development of next-generation diagnostics and therapeutics for cancer, in particular for metastasis.

            Cover page: Targeting Biophysical Cues: a Niche Approach to Study, Diagnose, and Treat Cancer.
            • Article
            • Peer Reviewed
            UC Riverside Previously Published Works (2016)
            © 2015 Elsevier Ltd. Manually collecting, identifying, archiving and retrieving specimen images is an expensive and time-consuming work for entomologists. There is a clear need to introduce fast systems integrated with modern image processing and analysis algorithms to accelerate the process. In this paper, we describe the development of an automated moth species identification and retrieval system (SPIR) using computer vision and pattern recognition techniques. The core of the system is a probabilistic model that infers Semantically Related Visual (SRV) attributes from low-level visual features of moth images in the training set, where moth wings are segmented into information-rich patches from which the local features are extracted, and the SRV attributes are provided by human experts as ground-truth. For the large amount of unlabeled test images in the database or added into the database later on, an automated identification process is evoked to translate the detected salient regions of low-level visual features on the moth wings into meaningful semantic SRV attributes. We further propose a novel network analysis based approach to explore and utilize the co-occurrence patterns of SRV attributes as contextual cues to improve individual attribute detection accuracy. Working with a small set of labeled training images, the approach constructs a network with nodes representing the SRV attributes and weighted edges denoting the co-occurrence correlation. A fast modularity maximization algorithm is proposed to detect the co-occurrence patterns as communities in the network. A random walk process working on the discovered co-occurrence patterns is applied to refine the individual attribute detection results. The effectiveness of the proposed approach is evaluated in automated moth identification and attribute-based image retrieval. In addition, a novel image descriptor called SRV attribute signature is introduced to record the visual and semantic properties of an image and is used to compare image similarity. Experiments are performed on an existing entomology database to illustrate the capabilities of our proposed system. We observed that the system performance is improved by the SRV attribute representation and their co-occurrence patterns.
              • Peer Reviewed
              UCLA Previously Published Works (1995)
                Cover page: Loss and Gain of Regularity in a Lubrication Equation for Thin Viscous Films
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2019)
                BACKGROUND: This study aims to evaluate the quality of preclinical data, determine the effect sizes, and identify experimental measures that inform efficacy using mesenchymal stromal (or stem) cells (MSC) therapy in animal models of rheumatoid arthritis (RA). METHODS: Literature searches were performed on MSC preclinical studies to treat RA. MSC treatment effect sizes were determined by the most commonly used outcome measures, including paw thickness, clinical score, and histological score. FINDINGS: A total of 48 studies and 94 treatment arms were included, among which 42 studies and 79 treatment arms reported that MSC improved outcomes. The effect sizes of RA treatments using MSC, when compared to the controls, were: paw thickness was ameliorated by 53.6% (95% confidence interval (CI): 26.7% -80.4%), histological score was decreased by 44.9% (95% CI: 33.3% -56.6%), and clinical score was decreased by 29.9% (95% CI: 16.7% -43.0%). Specifically, our results indicated that human umbilical cord derived MSC led to large improvements of the clinical score (-42.1%) and histological score (-51.4%). INTERPRETATION: To the best of our knowledge, this meta-analysis is to quantitatively answer whether MSC represent a robust RA treatment in animal models. It suggests that in preclinical studies, MSC have consistently exhibited therapeutic benefits. The findings demonstrate a need for considering variations in different animal models and treatment protocols in future studies using MSC to treat RA in humans to maximise the therapeutic gains in the era of precision medicine. FUNDS: NIH [1DP2CA195763], Baylx Inc.: BI-206512, NINDS/NIH Training Grant [Award# NS082174].
                  Cover page: Meta-analysis of preclinical studies of mesenchymal stromal cells to treat rheumatoid arthritis.Creative Commons 'BY-NC-ND' version 4.0 license
                  • Article
                  • Peer Reviewed
                  UC Irvine Previously Published Works (2020)
                  Many cancers metastasize to the bones, particularly in cases of breast and prostate cancers. Due to the "vicious cycle" of cancer cells inducing bone resorption, which promotes further tumor growth, they are difficult to treat and may lead to extreme pain. These factors increase the urgency for emerging therapeutics that target bone metastases more specifically and effectively. Animal studies are essential to the development of any therapeutics, but also require robust animal models of human diseases. Robust animal models are often challenging to develop in the case of bone metastasis studies. Previous methods to induce bone metastasis include intracardiac, intravenous, subcutaneous via mammary fat pad, and intraosseous cancer cell injections, but these methods all have limitations. By contrast, the caudal artery route of injection offers more robust bone metastasis, while also resulting in a lower rate of vital organ metastases than that of other routes of tumor implantation. A syngeneic animal model of bone metastasis is necessary in many cancer studies, because it allows the use of immunocompetent animals, which more accurately mimic cancer development observed in immunocompetent humans. Here we present a detailed method to generate robust and easily monitored 4T1-CLL1 syngeneic bone metastases with over 95% occurrence in BALB/c mice, within two weeks. This method can potentially increase consistency between animals in bone cancer metastasis studies and reduce the number of animals needed for studying bone metastases in mice.
                    Cover page: Optimization of a syngeneic murine model of bone metastasisCreative Commons 'BY' version 4.0 license
                    • Article
                    • Peer Reviewed
                    UC Irvine Previously Published Works (2017)

                    Background

                    Mechanical and biophysical properties of the cellular microenvironment regulate cell fate decisions. Mesenchymal stem cell (MSC) fate is influenced by past mechanical dosing (memory), but the mechanisms underlying this process have not yet been well defined. We have yet to understand how memory affects specific cell fate decisions, such as the differentiation of MSCs into neurons, adipocytes, myocytes, and osteoblasts.

                    Results

                    We study a minimal gene regulatory network permissive of multi-lineage MSC differentiation into four cell fates. We present a continuous model that is able to describe the cell fate transitions that occur during differentiation, and analyze its dynamics with tools from multistability, bifurcation, and cell fate landscape analysis, and via stochastic simulation. Whereas experimentally, memory has only been observed during osteogenic differentiation, this model predicts that memory regions can exist for each of the four MSC-derived cell lineages. We can predict the substrate stiffness ranges over which memory drives differentiation; these are directly testable in an experimental setting. Furthermore, we quantitatively predict how substrate stiffness and culture duration co-regulate the fate of a stem cell, and we find that the feedbacks from the differentiating MSC onto its substrate are critical to preserve mechanical memory. Strikingly, we show that re-seeding MSCs onto a sufficiently soft substrate increases the number of cell fates accessible.

                    Conclusions

                    Control of MSC differentiation is crucial for the success of much-lauded regenerative therapies based on MSCs. We have predicted new memory regions that will directly impact this control, and have quantified the size of the memory region for osteoblasts, as well as the co-regulatory effects on cell fates of substrate stiffness and culture duration. Taken together, these results can be used to develop novel strategies to better control the fates of MSCs in vitro and following transplantation.
                      Cover page: A mathematical model of mechanotransduction reveals how mechanical memory regulates mesenchymal stem cell fate decisionsCreative Commons 'BY' version 4.0 license
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