In this paper, we present an algorithm to estimate a signal from its modified short-time Fourier transform (STFT). This algorithm is computationally simple and is obtained by minimizing the mean squared error between the STFT of the estimated signal and the modified STFT. Using this algorithm, we also develop an iterative algorithm to estimate a signal from its modified STFT magnitude. The iterative algorithm is shown to decrease, in each iteration, the mean squared error between the STFT magnitude of the estimated signal and the modified STFT magnitude. The major computation involved in the iterative algorithm is the discrete Fourier transform (DFT) computation, and the algorithm appears to be real-time implementable with current hardware technology. The algorithm developed in this paper has been applied to the timescale modification of speech. The resulting system generates very high-quality speech, and appears to be better in performancc than any existing method.

Abstract-In this paper, we present an algorithm to estimate a signal from its modified short-time Fourier transform (STFT). This algorithm is computationally simple and is obtained by minimizing the mean squared error between the STFT of the estimated signal and the modified STFT. Using this algorithm, we also develop an iterative algorithm to estimate a signal from its modified STFT magnitude. The iterative algorithm is shown to decrease, in each iteration, the mean squared error between the STFT magnitude of the estimated signal and the modified STFT magnitude. The major computation involved in the iterative algorithm is the discrete Fourier transform (DFT) computation, and the algorithm appears to be real-time implementable with current hardware technology. The algorithm developed in this paper has been applied to the time-scale modification of speech. The resulting system generates very high-quality speech, and appears to be better in performancc than any existing method.

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Progesterone receptor membrane component 1 (PGRMC1) and PGRMC2 are expressed in rat granulosa cells and spontaneously immortalized granulosa cells (SIGCs) but their biological roles are not well defined. The present studies demonstrate that depleting either Pgrmc1 or Pgrmc2 in SIGCs increases entry into the cell cycle but does not increase cell proliferation. Rather, PGRMC1 and/or PGRMC2-deplete cells accumulate in metaphase and undergo apoptosis. Because both PGRMC1 and PGRMC2 localize to the mitotic spindle, their absence likely accounts for cells arresting in metaphase. Moreover, pull-down assays, colocalization studies and in situ proximity ligation assays (PLA) indicate that PGRMC1 binds PGRMC2. Disrupting the PGRMC1:PGRMC2 complex through the use of siRNA or the cytoplasmic delivery of a PGRMC2 antibody increases entry into the cell cycle. Conversely, overexpressing either PGRMC1-GFP or GFP-PGRMC2 fusion protein inhibits entry into the cell cycle. Subsequent studies reveal that depleting PGRMC1 and/or PGRMC2 reduces the percentage of cells in G0 and increases the percentage of cells in G1. These observations indicate that in addition to their role at metaphase, PGRMC1 and PGRMC2 are involved in regulating entry into the G1 stage of the cell cycle. Interestingly, both PGRMC1 and PGRMC2 bind GTPase-activating protein-binding protein 2 (G3BP2) as demonstrated by pull-down assays, colocalization assays, and PLAs. G3bp2 siRNA treatment also promotes entry into the G1 stage. This implies that dynamic changes in the interaction among PGRMC1, PGRMC2, and G3BP2 play an important protein regulating the rate at which SIGCs enter into the cell cycle.