Hedgehog (Hh) has been known as the only cholesterol-modified morphogen playing pivotal roles in development and tumorigenesis. A major unsolved question is how Hh signaling regulates the activity of Smoothened (SMO). Here, we performed an unbiased biochemical screen and identified that SMO was covalently modified by cholesterol on the Asp95 (D95) residue through an ester bond. This modification was inhibited by Patched-1 (Ptch1) but enhanced by Hh. The SMO(D95N) mutation, which could not be cholesterol modified, was refractory to Hh-stimulated ciliary localization and failed to activate downstream signaling. Furthermore, homozygous Smo (the equivalent residue in mouse) knockin mice were embryonic lethal with severe cardiac defects, phenocopying the Smo mice. Together, the results of our study suggest that Hh signaling transduces to SMO through modulating its cholesterylation and provides a therapeutic opportunity to treat Hh-pathway-related cancers by targeting SMO cholesterylation.

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Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an extended Halanay differential inequality and a new delay impulsive differential inequality, some sufficient conditions are derived, which depend on impulsive and coupling delays to guarantee the exponential synchronization of the memristor-based recurrent neural networks. Impulses with and without delay and time-varying delay are considered for modeling the coupled neural networks simultaneously, which renders more practical significance of our current research. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.

Compressive sensing (CS) has been widely studied and applied in many fields. Recently, the way to perform secure compressive sensing (SCS) has become a topic of growing interest. The existing works on SCS usually take the sensing matrix as a key and the resultant security level is not evaluated in depth. They can only be considered as a preliminary exploration on SCS, but a concrete and operable encipher model is not given yet. In this paper, we are going to investigate SCS in a systematic way. The relationship between CS and symmetric-key cipher indicates some possible encryption models. To this end, we propose the twolevel protection models (TLPM) for SCS which are developed from measurements taking and "something else", respectively. It is believed that these models will provide a new point of view and stimulate further research in both CS and cryptography. Specifically, an efficient and secure encryption scheme for parallel compressive sensing (PCS) is designed by embedding a two-layer protection in PCS using chaos. The first layer is undertaken by random permutation on a two-dimensional signal, which is proved to be an acceptable permutation with overwhelming probability. The other layer is to sample the permuted signal column by column with the same chaotic measurement matrix, which satisfies the restricted isometry property of PCS with overwhelming probability. Both the random permutation and the measurement matrix are constructed under the control of a chaotic system. Simulation results show that unlike the general joint compression and encryption schemes in which encryption always leads to the same or a lower compression ratio, the proposed approach of embedding encryption in PCS actually improves the compression performance. Besides, the proposed approach possesses high transmission robustness against additive Gaussian white noise and cropping attack.Index Terms-Secure compressive sensing, two-level protection models, symmetric-key cipher, parallel compressive sensing, random permutation, chaotic measurement matrix.