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In JoVE (1)
Other Publications (13)
- IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council
- IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
- IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
- Neuroscience Letters
- IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council
- IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council
- IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
- Neural Computation
- IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council
- PloS One
- PloS One
- Cancer Research
- Journal of Immunology (Baltimore, Md. : 1950)
Articles by Xiaolin Hu in JoVE
JoVE Immunology and Infection
Experimental Metastasis and CTL Adoptive Transfer Immunotherapy Mouse Model
Department of Biochemistry and Molecular Biology, Medical College of Georgia
An experimental lung metastasis and CTL immunotherapy mouse model for analysis of tumor cells-T cell interaction in vivo.
Other articles by Xiaolin Hu on PubMed
Solving Pseudomonotone Variational Inequalities and Pseudoconvex Optimization Problems Using the Projection Neural Network
In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network.
A Recurrent Neural Network for Solving a Class of General Variational Inequalities
This paper presents a recurrent neural-network model for solving a special class of general variational inequalities (GVIs), which includes classical VIs as special cases. It is proved that the proposed neural network (NN) for solving this class of GVIs can be globally convergent, globally asymptotically stable, and globally exponentially stable under different conditions. The proposed NN can be viewed as a modified version of the general projection NN existing in the literature. Several numerical examples are provided to demonstrate the effectiveness and performance of the proposed NN.
Design of General Projection Neural Networks for Solving Monotone Linear Variational Inequalities and Linear and Quadratic Optimization Problems
Most existing neural networks for solving linear variational inequalities (LVIs) with the mapping Mx + p require positive definiteness (or positive semidefiniteness) of M. In this correspondence, it is revealed that this condition is sufficient but not necessary for an LVI being strictly monotone (or monotone) on its constrained set where equality constraints are present. Then, it is proposed to reformulate monotone LVIs with equality constraints into LVIs with inequality constraints only, which are then possible to be solved by using some existing neural networks. General projection neural networks are designed in this correspondence for solving the transformed LVIs. Compared with existing neural networks, the designed neural networks feature lower model complexity. Moreover, the neural networks are guaranteed to be globally convergent to solutions of the LVI under the condition that the linear mapping Mx + p is monotone on the constrained set. Because quadratic and linear programming problems are special cases of LVI in terms of solutions, the designed neural networks can solve them efficiently as well. In addition, it is discovered that the designed neural network in a specific case turns out to be the primal-dual network for solving quadratic or linear programming problems. The effectiveness of the neural networks is illustrated by several numerical examples.
Acupuncture Improves Cognitive Deficits and Regulates the Brain Cell Proliferation of SAMP8 Mice
Senescence-accelerated mouse prone 8 (SAMP8) is an autogenic senile strain characterized by early cognitive impairment and age-related deterioration of learning and memory. To investigate the effect of acupuncture on behavioral changes and brain cell events, male 4-month-old SAMP8 and age-matched homologous normal aging SAMR1 mice were divided into four groups: SAMP8 acupuncture group (Pa), SAMP8 non-acupoint control group (Pn), SAMP8 control group (Pc) and SAMR1 normal control group (Rc). By Morris water maze test, the cognitive deficit of SAMP8 was revealed and significantly improved by "Yiqitiaoxue and Fubenpeiyuan" acupuncture. Meanwhile, by 5'-bromo-2'-deoxyuridine (BrdU) specific immunodetection, the decreased cell proliferation in dentate gyrus (DG) of SAMP8 was greatly enhanced by the therapeutic acupuncture, suggesting acupoint-related specificity. Even though no significant differences were found in ventricular/subventricular zones (VZ/SVZ) of the third ventricle (V3) and lateral ventricle (LV) between groups, we obtained interesting results: a stream-like distribution of newly proliferated cells presented along the dorsum of alveus hippocampi (Alv), extending from LV to corpus callosum (CC), and the therapeutic acupuncture showed a marked effect on this region. Our research suggests that acupuncture can induce different cell proliferation in different brain regions of SAMP8, which brings forth the need to explore further for the mechanism of cognitive deficits and acupuncture intervention in this field.
An Improved Dual Neural Network for Solving a Class of Quadratic Programming Problems and Its K-winners-take-all Application
This paper presents a novel recurrent neural network for solving a class of convex quadratic programming (QP) problems, in which the quadratic term in the objective function is the square of the Euclidean norm of the variable. This special structure leads to a set of simple optimality conditions for the problem, based on which the neural network model is formulated. Compared with existing neural networks for general convex QP, the new model is simpler in structure and easier to implement. The new model can be regarded as an improved version of the dual neural network in the literature. Based on the new model, a simple neural network capable of solving the k-winners-take-all ( k-WTA) problem is formulated. The stability and global convergence of the proposed neural network is proved rigorously and substantiated by simulation results.
A New Recurrent Neural Network for Solving Convex Quadratic Programming Problems with an Application to the K-winners-take-all Problem
In this paper, a new recurrent neural network is proposed for solving convex quadratic programming (QP) problems. Compared with existing neural networks, the proposed one features global convergence property under weak conditions, low structural complexity, and no calculation of matrix inverse. It serves as a competitive alternative in the neural network family for solving linear or quadratic programming problems. In addition, it is found that by some variable substitution, the proposed network turns out to be an existing model for solving minimax problems. In this sense, it can be also viewed as a special case of the minimax neural network. Based on this scheme, a k-winners-take-all ( k-WTA) network with O(n) complexity is designed, which is characterized by simple structure, global convergence, and capability to deal with some ill cases. Numerical simulations are provided to validate the theoretical results obtained. More importantly, the network design method proposed in this paper has great potential to inspire other competitive inventions along the same line.
An Alternative Recurrent Neural Network for Solving Variational Inequalities and Related Optimization Problems
There exist many recurrent neural networks for solving optimization-related problems. In this paper, we present a method for deriving such networks from existing ones by changing connections between computing blocks. Although the dynamic systems may become much different, some distinguished properties may be retained. One example is discussed to solve variational inequalities and related optimization problems with mixed linear and nonlinear constraints. A new network is obtained from two classical models by this means, and its performance is comparable to its predecessors. Thus, an alternative choice for circuits implementation is offered to accomplish such computing tasks.
A Gaussian Attractor Network for Memory and Recognition with Experience-dependent Learning
Attractor networks are widely believed to underlie the memory systems of animals across different species. Existing models have succeeded in qualitatively modeling properties of attractor dynamics, but their computational abilities often suffer from poor representations for realistic complex patterns, spurious attractors, low storage capacity, and difficulty in identifying attractive fields of attractors. We propose a simple two-layer architecture, gaussian attractor network, which has no spurious attractors if patterns to be stored are uncorrelated and can store as many patterns as the number of neurons in the output layer. Meanwhile the attractive fields can be precisely quantified and manipulated. Equipped with experience-dependent unsupervised learning strategies, the network can exhibit both discrete and continuous attractor dynamics. A testable prediction based on numerical simulations is that there exist neurons in the brain that can discriminate two similar stimuli at first but cannot after extensive exposure to physically intermediate stimuli. Inspired by this network, we found that adding some local feedbacks to a well-known hierarchical visual recognition model, HMAX, can enable the model to reproduce some recent experimental results related to high-level visual perception.
Design of Recurrent Neural Networks for Solving Constrained Least Absolute Deviation Problems
Recurrent neural networks for solving constrained least absolute deviation (LAD) problems or L(1)-norm optimization problems have attracted much interest in recent years. But so far most neural networks can only deal with some special linear constraints efficiently. In this paper, two neural networks are proposed for solving LAD problems with various linear constraints including equality, two-sided inequality and bound constraints. When tailored to solve some special cases of LAD problems in which not all types of constraints are present, the two networks can yield simpler architectures than most existing ones in the literature. In particular, for solving problems with both equality and one-sided inequality constraints, another network is invented. All of the networks proposed in this paper are rigorously shown to be capable of solving the corresponding problems. The different networks designed for solving the same types of problems possess the same structural complexity, which is due to the fact these architectures share the same computing blocks and only differ in connections between some blocks. By this means, some flexibility for circuits realization is provided. Numerical simulations are carried out to illustrate the theoretical results and compare the convergence rates of the networks.
IFN-γ Upregulates Survivin and Ifi202 Expression to Induce Survival and Proliferation of Tumor-specific T Cells
A common procedure in human cytotoxic T lymphocyte (CTL) adoptive transfer immunotherapy is to expand tumor-specific CTLs ex vivo using CD3 mAb prior to transfer. One of the major obstacles of CTL adoptive immunotherapy is a lack of CTL persistence in the tumor-bearing host after transfer. The aim of this study is to elucidate the molecular mechanisms underlying the effects of stimulation conditions on proliferation and survival of tumor-specific CTLs.
TNFα Cooperates with IFN-γ to Repress Bcl-xL Expression to Sensitize Metastatic Colon Carcinoma Cells to TRAIL-mediated Apoptosis
TNF-related apoptosis-inducing ligand (TRAIL) is an immune effector molecule that functions as a selective anti-tumor agent. However, tumor cells, especially metastatic tumor cells often exhibit a TRAIL-resistant phenotype, which is currently a major impediment in TRAIL therapy. The aim of this study is to investigate the synergistic effect of TNFα and IFN-γ in sensitizing metastatic colon carcinoma cells to TRAIL-mediated apoptosis.
IRF8 Regulates Acid Ceramidase Expression to Mediate Apoptosis and Suppresses Myelogeneous Leukemia
IFN regulatory factor 8 (IRF8) is a key transcription factor for myeloid cell differentiation and its expression is frequently lost in hematopoietic cells of human myeloid leukemia patients. IRF8-deficient mice exhibit uncontrolled clonal expansion of undifferentiated myeloid cells that can progress to a fatal blast crisis, thereby resembling human chronic myelogeneous leukemia (CML). Therefore, IRF8 is a myeloid leukemia suppressor. Whereas the understanding of IRF8 function in CML has recently improved, the molecular mechanisms underlying IRF8 function in CML are still largely unknown. In this study, we identified acid ceramidase (A-CDase) as a general transcription target of IRF8. We demonstrated that IRF8 expression is regulated by IRF8 promoter DNA methylation in myeloid leukemia cells. Restoration of IRF8 expression repressed A-CDase expression, resulting in C16 ceramide accumulation and increased sensitivity of CML cells to FasL-induced apoptosis. In myeloid cells derived from IRF8-deficient mice, A-CDase protein level was dramatically increased. Furthermore, we demonstrated that IRF8 directly binds to the A-CDase promoter. At the functional level, inhibition of A-CDase activity, silencing A-CDase expression, or application of exogenous C16 ceramide sensitized CML cells to FasL-induced apoptosis, whereas overexpression of A-CDase decreased CML cells' sensitivity to FasL-induced apoptosis. Consequently, restoration of IRF8 expression suppressed CML development in vivo at least partially through a Fas-dependent mechanism. In summary, our findings determine the mechanism of IRF8 downregulation in CML cells and they determine a primary pathway of resistance to Fas-mediated apoptosis and disease progression.
Cutting Edge: IRF8 Regulates Bax Transcription in Vivo in Primary Myeloid Cells
A prominent phenotype of IRF8 knockout (KO) mice is the uncontrolled expansion of immature myeloid cells. The molecular mechanism underlying this myeloproliferative syndrome is still elusive. In this study, we observed that Bax expression level is low in bone marrow preginitor cells and increases dramatically in primary myeloid cells in wt mice. In contrast, Bax expression level remained at a low level in primarymyeloid cells in IRF8 KO mice. However, in vitro IRF8 KO bone marrow-differentiated myeloid cells expressed Bax at a level as high as that in wild type myeloid cells. Furthermore, we demonstrated that IRF8 specifically binds to the Bax promoter region in primary myeloid cells. Functional analysis indicated that IRF8 deficiency results in increased resistance of the primary myeloid cells to Fas-mediated apoptosis. Our findings show that IRF8 directly regulates Bax transcription in vivo, but not in vitro during myeloid cell lineage differentiation.
