arXiv subjects

Zi Chen

Publications and source records attributed to Zi Chen.

18 recordsLinked to original sources

arXiv2024

Top-r Influential Community Search in Bipartite Graphs

Community search on bipartite graphs, especially influential community detection, has received significant attention. Existing studies use minimum vertex weights, inadequately reflecting true community influence when some vertices have low weights. To address this, we introduce the $(\alpha,\beta)$-influential community model based on the average vertex weights from both layers, providing a more comprehensive influence measure. Given the NP-hardness of accurately identifying top-$r$ communities, we propose an exact recursive algorithm enhanced by a slim tree structure and upper-bound techniques to improve efficiency. Additionally, we develop a greedy approximate algorithm with $O((n+m)+m\log{n})$ complexity, further optimized by a pruning strategy. Experiments on 10 real-world graphs demonstrate the effectiveness and efficiency of our proposed algorithms.

cs.SI↗

arXiv2024

Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks

Top-k Nearest Neighbors (kNN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have been proposed to speedup the query processing. However, even with the current state-of-the-art approach, long query processing delays persist, along with significant space overhead and prohibitively long indexing time. In this paper, we depart from the complex index designs prevalent in existing literature and propose a simple index named KNN-Index. With KNN-Index, we can answer a kNN query optimally and progressively with small and size-bounded index. To improve the index construction performance, we propose a bidirectional construction algorithm which can effectively share the common computation during the construction. Theoretical analysis and experimental results on real road networks demonstrate the superiority of KNN-Index over the state-of-the-art approach in query processing performance, index size, and index construction efficiency.

cs.DB↗

arXiv2024

Dimeric Drug Polymeric Micelles with Acid-Active Tumor Targeting and FRET-indicated Drug Release

Trans-activating transcriptional activator (TAT), a cell-penetrating peptide, has been extensively used for facilitating cellular uptake and nuclear targeting of drug delivery systems. However, the positively charged TAT peptide usually strongly interacts with serum components and undergoes substantial phagocytosis by the reticuloendothelial system, causing a short blood circulation in vivo. In this work, an acid-active tumor targeting nanoplatform DA-TAT-PECL was developed to effectively inhibit the nonspecific interactions of TAT in the bloodstream. 2,3-dimethylmaleic anhydride (DA) was first used to convert the TAT amines to carboxylic acid, the resulting DA-TAT was further conjugated to get DA-TAT-PECL. After self-assembly into polymeric micelles, they were capable of circulating in the physiological condition for a long time and promoting cell penetration upon accumulation at the tumor site and de-shielding the DA group. Moreover, camptothecin (CPT) was used as the anticancer drug and modified into a dimer (CPT)2-ss-Mal, in which two CPT molecules were connected by a reduction-labile maleimide thioether bond. The FRET signal between CPT and maleimide thioether bond was monitored to visualize the drug release process and effective targeted delivery of antitumor drugs was demonstrated. This pH/reduction dual-responsive micelle system provides a new platform for high fidelity cancer therapy.

q-bio.TO↗

arXiv2023

GPUSCAN$^{++}$:Efficient Structural Graph Clustering on GPUs

Structural clustering is one of the most popular graph clustering methods, which has achieved great performance improvement by utilizing GPUs. Even though, the state-of-the-art GPU-based structural clustering algorithm, GPUSCAN, still suffers from efficiency issues since lots of extra costs are introduced for parallelization. Moreover, GPUSCAN assumes that the graph is resident in the GPU memory. However, the GPU memory capacity is limited currently while many real-world graphs are big and cannot fit in the GPU memory, which makes GPUSCAN unable to handle large graphs. Motivated by this, we present a new GPU-based structural clustering algorithm, GPUSCAN++, in this paper. To address the efficiency issue, we propose a new progressive clustering method tailored for GPUs that not only avoid high parallelization costs but also fully exploits the computing resources of GPUs. To address the GPU memory limitation issue, we propose a partition-based algorithm for structural clustering that can process large graphs with limited GPU memory. We conduct experiments on real graphs, and the experimental results demonstrate that our algorithm can achieve up to 168 times speedup compared with the state-of-the-art GPU-based algorithm when the graph can be resident in the GPU memory. Moreover, our algorithm is scalable to handle large graphs. As an example, our algorithm can finish the structural clustering on a graph with 1.8 billion edges using less than 2 GB GPU memory.

cs.DB↗

arXiv2023

Applications of Raman Spectroscopy in Clinical Medicine

Raman spectroscopy provides spectral information related to the specific molecular structures of substances and has been well established as a powerful tool for studying biological tissues and diagnosing diseases. This article reviews recent advances in Raman spectroscopy and its applications in diagnosing various critical diseases, including cancers, infections, and neurodegenerative diseases, and in predicting surgical outcomes. These advances are explored through discussion of state-of-the-art forms of Raman spectroscopy, such as surface-enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip-enhanced Raman spectroscopy employed in biomedical sciences. We discuss biomedical applications, including various aspects and methods of ex vivo and in vivo medical diagnosis, sample collection, data processing, and achievements in realizing the correlation between Raman spectra and biochemical information in certain diseases. Finally, we present the limitations of the current study and provide perspectives for future research.

physics.app-ph↗

arXiv2023

Effective Community Search on Large Attributed Bipartite Graphs

Community search over bipartite graphs has attracted significant interest recently. In many applications such as user-item bipartite graph in E-commerce, customer-movie bipartite graph in movie rating website, nodes tend to have attributes, while previous community search algorithm on bipartite graphs ignore attributes, which makes the returned results with poor cohesion with respect to their node attributes. In this paper, we study the community search problem on attributed bipartite graphs. Given a query vertex q, we aim to find attributed $\left(\alpha,\beta\right)$-communities of $G$, where the structure cohesiveness of the community is described by an $\left(\alpha,\beta\right)$-core model, and the attribute similarity of two groups of nodes in the subgraph is maximized. In order to retrieve attributed communities from bipartite graphs, we first propose a basic algorithm composed of two steps: the generation and verification of candidate keyword sets, and then two improved query algorithms Inc and Dec are proposed. Inc is proposed considering the anti-monotonity property of attributed bipartite graphs, then we adopt different generating method and verifying order of candidate keyword sets and propose the Dec algorithm. After evaluating our solutions on eight large graphs, the experimental results demonstrate that our methods are effective and efficient in querying the attributed communities on bipartite graphs.

cs.SI↗

arXiv2022

Mechanics of Morphogenesis in Neural Development: in vivo, in vitro, and in silico

Morphogenesis in the central nervous system has received intensive attention as elucidating fundamental mechanisms of morphogenesis will shed light on the physiology and pathophysiology of the developing central nervous system. Morphogenesis of the central nervous system is of a vast topic that includes important morphogenetic events such as neurulation and cortical folding. Here we review three types of methods used to improve our understanding of morphogenesis of the central nervous system: in vivo experiments, organoids (in vitro), and computational models (in silico). The in vivo experiments are used to explore cellular- and tissue-level mechanics and interpret them on the roles of neurulation morphogenesis. Recent advances in human brain organoids have provided new opportunities to study morphogenesis and neurogenesis to compensate for the limitations of in vivo experiments, as organoid models are able to recapitulate some critical neural morphogenetic processes during early human brain development. Due to the complexity and costs of in vivo and in vitro studies, a variety of computational models have been developed and used to explain the formation and morphogenesis of brain structures. We review and discuss the Pros and Cons of these methods and their usage in the studies on morphogenesis of the central nervous system. Notably, none of these methods alone is sufficient to unveil the biophysical mechanisms of morphogenesis, thus calling for the interdisciplinary approaches using a combination of these methods in order to test hypotheses and generate new insights on both normal and abnormal development of the central nervous system.

q-bio.NC↗

arXiv2022

Synthetically Non-Hermitian Nonlinear Wave-like Behavior in a Topological Mechanical Metamaterial

Topological mechanical metamaterials have enabled new ways to control stress and deformation propagation. Exemplified by Maxwell lattices, they have been studied extensively using a linearized formalism. Herein, we study a two-dimensional topological Maxwell lattice by exploring its large deformation quasi-static response using geometric numerical simulations and experiments. We observe spatial nonlinear wave-like phenomena such as harmonic generation, localized domain switching, amplification-enhanced frequency conversion, and solitary waves. We further map our linearized, homogenized system to a non-Hermitian, non-reciprocal, one-dimensional wave equation, revealing an equivalence between the deformation fields of two-dimensional topological Maxwell lattices and nonlinear dynamical phenomena in one-dimensional active systems. Our study opens a new regime for topological mechanical metamaterials and expands their application potential in areas including adaptive and smart materials, and mechanical logic, wherein concepts from nonlinear dynamics may be used to create intricate, tailored spatial deformation and stress fields greatly exceeding conventional elasticity.

cond-mat.soft↗

arXiv2022

Multistable Topological Mechanical Metamaterials

Concepts from quantum topological states of matter have been extensively utilized in the past decade in creating mechanical metamaterials with topologically protected features, such as one-way edge states and topologically polarized elasticity. Maxwell lattices represent a class of topological mechanical metamaterials that exhibit distinct robust mechanical properties at edges/interfaces when they are topologically polarized. Realizing topological phase transitions in these materials would enable on-and-off switching of these edge states, opening unprecedented opportunities to program mechanical response and wave propagation. However, such transitions are extremely challenging to experimentally control in Maxwell topological metamaterials due to mechanical and geometric constraints. Here we create a Maxwell lattice with bistable units to implement synchronized transitions between topological states and demonstrate dramatically different stiffnesses as the lattice transforms between topological phases both theoretically and experimentally. By combining multistability with topological phase transitions, for the first time, this metamaterial not only exhibits topologically protected mechanical properties that swiftly and reversibly change, but also offers a rich design space for innovating mechanical computing architectures and reprogrammable neuromorphic metamaterials. Moreover, we design and fabricate a topological Maxwell lattice using multi-material 3D printing and demonstrate the potential for miniaturization via additive manufacturing. These design principles are applicable to transformable topological metamaterials for a variety of tasks such as switchable energy absorption, impact mitigation, wave tailoring, neuromorphic metamaterials, and controlled morphing systems.

physics.app-ph↗

arXiv2022

Balanced Clique Computation in Signed Networks: Concepts and Algorithms

Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks. However, in real world, many applications are modeled as signed networks with positive and negative edges. As the signed networks hold their own properties different from the unsigned networks, the existing clique model is inapplicable for the signed networks. Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks. Following the balanced clique model, we study the maximal balanced clique enumeration problem (MBCE) which computes all the maximal balanced cliques in a given signed network and the maximum balanced clique search problem (MBCS) which computes the balanced clique with maximum size. We show that MBCE problem and MBCS problem are both NP-Hard. For the MBCE problem, a straightforward solution is to treat the signed network as two unsigned networks and leverage the off-the-shelf techniques for unsigned networks. However, such a solution is inefficient for large signed networks. To address this problem, in this paper, we first propose a new maximal balanced clique enumeration algorithm by exploiting the unique properties of signed networks. Based on the new proposed algorithm, we devise two optimization strategies to further improve the efficiency of the enumeration. For the MBCS problem, we propose a new search framework based on search space partition. To further improve the efficiency of the new framework, we propose multiple optimization strategies regarding to redundant search branches and invalid candidates. We conduct extensive experiments on large real datasets. The experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms for MBCE problem and MBCS problem.

cs.DS↗

arXiv2022

Higher-Order Neighborhood Truss Decomposition

$k$-truss model is a typical cohesive subgraph model and has been received considerable attention recently. However, the $k$-truss model only considers the direct common neighbors of an edge, which restricts its ability to reveal fine-grained structure information of the graph. Motivated by this, in this paper, we propose a new model named $(k, τ)$-truss that considers the higher-order neighborhood ($τ$ hop) information of an edge. Based on the $(k, τ)$-truss model, we study the higher-order truss decomposition problem which computes the $(k, τ)$-trusses for all possible $k$ values regarding a given $τ$. Higher-order truss decomposition can be used in the applications such as community detection and search, hierarchical structure analysis, and graph visualization. To address this problem, we first propose a bottom-up decomposition paradigm in the increasing order of $k$ values to compute the corresponding $(k, τ)$-truss. Based on the bottom-up decomposition paradigm, we further devise three optimization strategies to reduce the unnecessary computation. We evaluate our proposed algorithms on real datasets and synthetic datasets, the experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms.

cs.DB↗

arXiv2019

Biomechanics of Collective Cell Migration in Cancer Progression -- Experimental and Computational Methods

Cell migration is essential for regulating many biological processes in physiological or pathological conditions, including embryonic development and cancer invasion. In vitro and in silico studies suggest that collective cell migration is associated with some biomechanical particularities, such as restructuring of extracellular matrix, stress and force distribution profiles, and reorganization of cytoskeleton. Therefore, the phenomenon could be understood by an in-depth study of cells' behavior determinants, including but not limited to mechanical cues from the environment and from fellow travelers. This review article aims to cover the recent development of experimental and computational methods for studying the biomechanics of collective cell migration during cancer progression and invasion. We also summarized the tested hypotheses regarding the mechanism underlying collective cell migration enabled by these methods. Together, the paper enables a broad overview on the methods and tools currently available to unravel the biophysical mechanisms pertinent to cell collective migration, as well as providing perspectives on future development towards eventually deciphering the key mechanisms behind the most lethal feature of cancer.

q-bio.CB↗

arXiv2015

Mechanical Self-Assembly of a Strain-Engineered Flexible Layer: Wrinkling, Rolling, and Twisting

Self-shaping of curved structures, especially those involving flexible thin layers, has attracted increasing attention because of their broad potential applications in e.g. nanoelectromechanical/micro-electromechanical systems (NEMS/MEMS), sensors, artificial skins, stretchable electronics, robotics, and drug delivery. Here, we provide an overview of recent experimental, theoretical, and computational studies on the mechanical self-assembly of strain-engineered thin layers, with an emphasis on systems in which the competition between bending and stretchingenergy gives rise to a variety ofdeformations,such as wrinkling, rolling, and twisting. We address the principle of mechanical instabilities, which is often manifested in wrinkling or multistability of strain-engineered thin layers. The principles of shape selection and transition in helical ribbons are also systematically examined. We hope that a more comprehensive understanding of the mechanical principles underlying these rich phenomena can foster the development of new techniques for manufacturing functional three- dimensional structures on demand for a broad spectrum of engineering applications.

cond-mat.soft↗

arXiv2015

Fast nastic motion of plants and bio-inspired structures

The capability to sense and respond to external mechanical stimuli at various timescales is essential to many physiological aspects in plants, including self-protection, intake of nutrients, and reproduction. Remarkably, some plants have evolved the ability to react to mechanical stimuli within a few seconds despite a lack of muscles and nerves. The fast movements of plants in response to mechanical stimuli have long captured the curiosity of scientists and engineers, but the mechanisms behind these rapid thigmonastic movements still are not understood completely. In this article, we provide an overview of such thigmonastic movements in several representative plants, including Dionaea, Utricularia, Aldrovanda, Drosera, and Mimosa. In addition, we review a series of studies that present biomimetic structures inspired by fast moving plants. We hope that this article will shed light on the current status of research on the fast movements of plants and bioinspired structures and also promote interdisciplinary studies on both the fundamental mechanisms of plants' fast movements and biomimetic structures for engineering applications, such as artificial muscles, multi-stable structures, and bioinspired robots.

physics.bio-ph↗

arXiv2013

Shape Selection and Multi-stability in Helical Ribbons

Helical structures, almost ubiquitous in biological systems, have inspired the design and manufacturing of helical devices with applications in nanoelecromechanical systems (NEMS), morphing structures, optoelectronics, micro-robotics and drug delivery devices. Meanwhile, multi-stable structures, represented by the Venus flytrap and slap bracelet, have attracted increasing attention due to their applications in making artificial muscles, bio-inspired robots, deployable aerospace components and energy harvesting devices. Here we show that the mechanical anisotropy pertinent to helical deformation, together with geometric nonlinearity associated with multi-stability, can lead to novel selection principle of the geometric shape and multi-stability in spontaneous helical ribbons. Simple table-top experiments were also performed to illustrate the working principle. Our work will promote understanding of spontaneous curling, twisting, wrinkling of thin objects and their instabilities, and serve as a tool in developing functional structures and devices with tunable, morphing geometries and smart actuation mechanism that can be applied in a spectrum of areas.

cond-mat.soft↗

arXiv2013

Mechanics of Tunable Helices and Geometric Frustration in Biomimetic Seashells

Helical structures are ubiquitous in nature and engineering, ranging from DNA molecules to plant tendrils, from sea snail shells to nanoribbons. While the helical shapes in natural and engineered systems often exhibit nearly uniform radius and pitch, helical shell structures with changing radius and pitch, such as seashells and some plant tendrils, adds to the variety of this family of aesthetic beauty. Here we develop a comprehensive theoretical framework for tunable helical morphologies, and report the first biomimetic seashell-like structure resulting from mechanics of geometric frustration. In previous studies, the total potential energy is everywhere minimized when the system achieves equilibrium. In this work, however, the local energy minimization cannot be realized because of the geometric incompatibility, and hence the whole system deforms into a shape with a global energy minimum whereby the energy in each segment may not necessarily be locally optimized. This novel approach can be applied to develop materials and devices of tunable geometries with a range of applications in nano/biotechnology.

math-ph↗

arXiv2012

Continuum Elasticity Theory Approach for Spontaneous Bending and Twisting of Ribbons Induced by Mechanical Anisotropy

Helical ribbons arise in many biological and engineered systems, often driven by anisotropic surface stress, residual strain, and geometric or elastic mismatch between layers of a laminated composite. A full mathematical analysis is developed to analytically predict the equilibrium deformed helical shape of an initially flat, straight ribbon, with prescribed magnitudes and orientations of the principal curvatures when subjected to arbitrary surface stress and/or internal residual strain distribution. The helix angle, radius, axis and chirality of the deformed helical ribbons are predicted with a comprehensive, three-dimensional model that incorporates elasticity, differential geometry, and variational principles. In general, the mechanical anisotropy (e.g., in surface/external stress, residual strain or elastic modulus) will lead to spontaneous, three-dimensional helical deformations. Ring shapes are formed when the principle axes of deformation coincide with the geometric axes of the ribbon. The transition from cylindrical helical ribbons to purely twisted ribbons, or tubular structures is controlled by tuning relevant geometric parameters. Analytic, closed-form predictions of the ribbon shapes are validated with simple, table-top experiments. This theoretical approach represent a tool to inform the design of materials and systems in order to achieve desired helical geometries on demand.

math-ph↗

arXiv2012

Nonlinear geometric effects in mechanical bistable morphing structures

Bistable structures associated with non-linear deformation behavior, exemplified by the Venus flytrap and slap bracelet, can switch between different functional shapes upon actuation. Despite numerous efforts in modeling such large deformation behavior of shells, the roles of mechanical and nonlinear geometric effects on bistability remain elusive. We demonstrate, through both theoretical analysis and table-top experiments, that two dimensionless parameters control bistability. Our work classifies the conditions for bistability, and extends the large deformation theory of plates and shells.

cond-mat.mtrl-sci↗