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Abstract-We consider sensor data scheduling for remote state estimation. Due to constrained communication energy and bandwidth, a sensor needs to decide whether it should send the measurement to a remote estimator for further processing. We propose an event-based sensor data scheduler for linear systems and derive the corresponding minimum squared error estimator. By selecting an appropriate eventtriggering threshold, we illustrate how to achieve a desired balance between the sensor-to-estimator communication rate and the estimation quality. Simulation examples are provided to demonstrate the theory.

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We propose an open-loop and a closed-loop stochastic event-triggered sensor schedule for remote state estimation. Both schedules overcome the essential difficulties of existing schedules in recent literature works where, through introducing a deterministic event-triggering mechanism, the Gaussian property of the innovation process is destroyed which produces a challenging nonlinear filtering problem that cannot be solved unless approximation techniques are adopted. The proposed stochastic event-triggered sensor schedules eliminate such approximations. Under these two schedules, the minimum mean squared error (MMSE) estimator and its estimation error covariance matrix at the remote estimator are given in a closed-form. The stability in terms of the expected error covariance and the sample path of the error covariance for both schedules is studied. We also formulate and solve an optimization problem to obtain the minimum communication rate under some estimation quality constraint using the open-loop sensor schedule. A numerical comparison between the closed-loop MMSE estimator and a typical approximate MMSE estimator with deterministic eventtriggered sensor schedule, in a problem setting of target tracking, shows the superiority of the proposed sensor schedule.

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A cell biology study using conditional gene knockout mouse models reveals a novel mechanism by which the actin cytoskeleton negatively regulates the signal transduction of the B cell antigen receptor.

This paper studies higher-order finite volume methods for solving elliptic boundary value problems. We develop a general framework for construction and analysis of higher-order finite volume methods. Specifically, we establish the boundedness and uniform ellipticity of the bilinear forms for the methods, and show that they lead to an optimal error estimate of the methods. We prove that the uniform local-ellipticity of the family of the bilinear forms ensures its uniform ellipticity. We then establish necessary and sufficient Communicated by Aihui Zhou. 192 Z. Chen et al.conditions for the uniform local-ellipticity in terms of geometric requirements on the meshes of the domain of the differential equation, and provide a general way to investigate the mesh geometric requirements for arbitrary higher-order schemes. Several useful examples of higher-order finite volume methods are presented to illustrate the mesh geometric requirements.

Activation of phosphatidylinositol 3 kinase (PI3K), Ras, and Her2 signaling plays a critical role in cancer development. Hotspot constitutive activating mutations in oncogenes, such as PIK3CA encoding the p110α catalytic subunit or RAS, as well as overexpression of Her2, are frequently found in human tumors and cancers. It has been well established that activation of these oncogenes profoundly promotes tumor metastasis, whereas decreased expression of ΔNp63α, the major protein isoform of the p53-related p63 expressed in epithelial cells, has been associated with cancer metastasis. In this study, we demonstrate that hotspot oncogenic mutations on PIK3CA and RAS, including p110α H1047R , K-Ras G12V , and H-Ras G12V , as well as activation of Her2, all led to suppression of ΔNp63α expression via Akt-fork-head transcription factor 3a (Akt-FOXO3a) signaling, resulting in increased cell motility and tumor metastasis. Expression of ΔNp63α effectively reversed p110α H1047R -, K-Ras G12V -, H-Ras G12V -, or Her2-induced cell motility in vitro and tumor metastasis in mouse models. We show that ΔNp63α was a direct FOXO3a transcriptional target and that expression of FOXO3a and ΔNp63α was correlated in human cancer biopsy samples. Together, these results demonstrate that ΔNp63α is a common inhibitory target of oncogenic PI3K, Ras, and Her2, and that ΔNp63α may function as a critical integrator of oncogenic signaling in cancer metastasis.PIK3CA | Ras | Her2 | ΔNp63α | cancer metastasis