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Summary Although the intrinsic mechanisms that control whether stem cells divide symmetrically or asymmetrically underlie tissue growth and homeostasis, they remain poorly defined. We report that the RNA-binding protein, Fragile X Mental Retardation Protein (FMRP), limits the symmetric division, and resulting expansion, of the stem cell population during adaptive intestinal growth in Drosophila. The elevated insulin sensitivity that FMRP-deficient progenitor cells display contributes to their accelerated expansion, which is suppressed by depletion of insulin signaling components. This FMRP activity is mediated solely via a second, conserved RNA-binding protein, LIN-28, known to boost insulin signaling in stem cells. Via LIN-28, FMRP controls progenitor cell behavior by post-transcriptionally repressing the level of Insulin Receptor (InR). This study identifies the stem cell based mechanism by which FMRP controls tissue adaptation, and raises the possibility that defective adaptive growth underlies the accelerated growth, gastrointestinal, and other symptoms that affect Fragile X Syndrome patients.

Quadrature compressive sampling (QuadCS) is a recentlyintroduced sub-Nyquist sampling scheme for effective acquisition of inphase and quadrature (I/Q) components of sparse radio frequency signals. In applications to pulse-Doppler radars, the QuadCS outputs can be arranged into a two-dimensional data format, in terms of slow time and virtual fast time, similar to that by Nyquist sampling. This paper develops a compressive sampling pulse-Doppler (CoSaPD) processing scheme which performs Doppler estimation/detection and range estimation from the sub-Nyquist data without recovering the Nyquist samples. The Doppler estimation is realized through a spectrum analyzer as in classical processing, whereas the detection is performed using the Doppler bin data. The range estimation is performed using sparse recovery algorithms only for the detected targets to reduce the computational load. A low detection threshold is used to improve the detection probability and the introduced false targets are then removed in the range estimation stage by exploiting the inherent target detection capability of the recovery algorithms. Simulation results verify the effectiveness of the proposed CoSaPD scheme, which requires only one-eighth of the Nyquist rate to achieve similar performance to the classical processing with Nyquist samples, provided that the input signal-to-noise ratio (SNR) is above −25 dB.

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We investigate the one-bit MIMO (1b-MIMO) radar that performs one-bit sampling with a timevarying threshold in the temporal domain and employs compressive sensing in the spatial and Doppler domains. The goals are to significantly reduce the hardware cost, energy consumption, and amount of stored data. The joint angle and Doppler frequency estimations from noisy one-bit data are studied.By showing that the effect of noise on one-bit sampling is equivalent to that of sparse impulsive perturbations, we formulate the one-bit ℓ 1 -regularized atomic-norm minimization (1b-ANM-L1) problem to achieve gridless parameter estimation with high accuracy. We also develop an iterative method for solving the 1b-ANM-L1 problem via the alternating direction method of multipliers. The Cramér-Rao bound (CRB) of the 1b-MIMO radar is analyzed, and the analytical performance of one-bit sampling with two different threshold strategies is discussed. Numerical experiments are presented to show that the 1b-MIMO radar can achieve high-resolution parameter estimation with a largely reduced amount of data.