Alpha-fetoprotein controls female fertility and prenatal development of the gonadotropin-releasing hormone pathway through an antiestrogenic action

It has been shown previously that female mice homozygous for an alpha-fetoprotein (AFP) null allele are sterile as a result of anovulation, probably due to a defect in the hypothalamic-pituitary axis. Here we show that these female mice exhibit specific anomalies in the expression of numerous genes in the pituitary, including genes involved in the gonadotropin-releasing hormone pathway, which are underexpressed. In the hypothalamus, the gonadotropin-releasing hormone gene, Gnrh1, was also found to be down-regulated. However, pituitary gene expression could be normalized and fertility could be rescued by blocking prenatal estrogen synthesis using an aromatase inhibitor. These results show that AFP protects the developing female brain from the adverse effects of prenatal estrogen exposure and clarify a long-running debate on the role of this fetal protein in brain sexual differentiation.

Alpha-fetoprotein protects the developing female mouse brain from masculinization and defeminization by estrogens

Two clearly opposing views exist on the function of alpha-fetoprotein (AFP), a fetal plasma protein that binds estrogens with high affinity, in the sexual differentiation of the rodent brain. AFP has been proposed to either prevent the entry of estrogens or to actively transport estrogens into the developing female brain. The availability of Afp mutant mice (Afp-/-) now finally allows us to resolve this longstanding controversy concerning the role of AFP in brain sexual differentiation, and thus to determine whether prenatal estrogens contribute to the development of the female brain. Here we show that the brain and behavior of female Afp-/- mice were masculinized and defeminized. However, when estrogen production was blocked by embryonic treatment with the aromatase inhibitor 1,4,6-androstatriene-3,17- dione, the feminine phenotype of these mice was rescued. These results clearly demonstrate that prenatal estrogens masculinize and defeminize the brain and that AFP protects the female brain from these effects of estrogens. © 2006 Nature Publishing Group.

Regulation of the alpha-fetoprotein promoter: Ku binding and DNA spatial conformation

This work shows that the proximal promoter of the mouse Afp gene contains a Ku binding site and that Ku binding is associated with down-regulation of the transcriptional activity of the Afp promoter. The Ku binding site is located in a segment able to adopt a peculiar structured form, probably a hairpin structure. Interestingly, the structured form eliminates the binding sites of the positive transcription factor HNF1. Furthermore, a DNAse hypersensitive site is detected in footprinting experiments done with extracts of AFP non-expressing hepatoma cells. These observations suggest that the structured form is stabilised by Ku and is associated with extinction of the gene in AFP non-expressing hepatic cells.

A morpho-molecular prognostic model for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third common cause of cancer-related deaths and its prognostication is still suboptimal. The aim of this study was to establish a new prognostication algorithm for HCC.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.