Foldons, protein structural modules, and exons.

Foldons, which are kinetically competent, quasi-independently folding units of a protein, may be defined using energy landscape analysis. Foldons can be identified by maxima in a scan of the ratio of a contiguous segment's energetic stability gap to the energy variance of that segment's molten globule states, reflecting the requirement of minimal frustration. The predicted foldons are compared with the exons and structural modules for 16 of the 30 proteins studied. Statistical analysis indicates a strong correlation between the energetically determined foldons and Go's geometrically defined structural modules, but there are marked sequence-dependent effects. There is only a weak correlation of foldons to exons. For gammaII-crystallin, myoglobin, barnase, alpha-lactalbumin, and cytochrome c the foldons and some noncontiguous clusters of foldons compare well with intermediates observed in experiment.

Apoptosis induced by a human milk protein.

To the breast-fed infant, human milk is more than a source of nutrients; it furnishes a wide array of molecules that restrict microbes, such as antibodies, bactericidins, and inhibitors of bacterial adherence. However, it has rarely been considered that human milk may also contain substances bioactive toward host cells. While investigating the effect of human milk on bacterial adherence to a human lung cancer cell line, we were surprised to discover that the milk killed the cells. Analysis of this effect revealed that a component of milk in a particular physical state--multimeric alpha-lact-albumin--is a potent Ca(2+)-elevating and apoptosis-inducing agent with broad, yet selective, cytotoxic activity. Multimeric alpha-lactalbumin killed all transformed, embryonic, and lymphoid cells tested but spared mature epithelial elements. These findings raise the possibility that milk contributes to mucosal immunity not only by furnishing antimicrobial molecules but also by policing the function of lymphocytes and epithelium. Finally, analysis of the mechanism by which multimeric alpha-lactalbumin induces apoptosis in transformed epithelial cells could lead to the design of antitumor agents.

Heat-induced and other chemical changes in commercial UHT milks

The properties of commercial directly and indirectly heated UHT milks, both after heating and during storage at room temperature for 24 weeks, were studied. Thermally induced changes were examined by changes in lactulose, furosine and acid-soluble whey proteins. The results confirmed previous reports that directly heated UHT milks suffer less heat damage than indirectly heated milk. During storage, furosine increased and bovine serum albumin in directly heat-treated milks decreased significantly. The changes in lactulose, alpha-lactalbumin and beta-lactoglobulin were not statistically significant. The data suggest that heat treatment indicators should be measured as soon as possible after processing to avoid any misinterpretations of the intensity of the heat treatment.

Analysis of human breast milk cells: gene expression profiles during pregnancy, lactation, involution, and mastitic infection

The molecular processes underlying human milk production and the effects of mastitic infection are largely unknown because of limitations in obtaining tissue samples. Determination of gene expression in normal lactating women would be a significant step toward understanding why some women display poor lactation outcomes. Here, we demonstrate the utility of RNA obtained directly from human milk cells to detect mammary epithelial cell (MEC)-specific gene expression. Milk cell RNA was collected from five time points (24 h prepartum during the colostrum period, midlactation, two involutions, and during a bout of mastitis) in addition to an involution series comprising three time points. Gene expression profiles were determined by use of human Affymetrix arrays. Milk cells collected during milk production showed that the most highly expressed genes were involved in milk synthesis (e.g., CEL, OLAH, FOLR1, BTN1A1, and ARG2), while milk cells collected during involution showed a significant downregulation of milk synthesis genes and activation of involution associated genes (e.g., STAT3, NF-kB, IRF5, and IRF7). Milk cells collected during mastitic infection revealed regulation of a unique set of genes specific to this disease state, while maintaining regulation of milk synthesis genes. Use of conventional epithelial cell markers was used to determine the population of MECs within each sample. This paper is the first to describe the milk cell transcriptome across the human lactation cycle and during mastitic infection, providing valuable insight into gene expression of the human mammary gland.

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.