Opposite phenotypes of hypomorphic and Y766 phosphorylation site mutations reveal a function for Fgfr1 in anteroposterior patterning of mouse embryos

Intercellular communication is needed for both the generation of the mesodermal germ layer and its division into distinct subpopulations. To dissect the functions of fibroblast growth factor receptor-1 (FGFR1) during mouse gastrulation as well as to gain insights into its possible roles during later embryonic development, we have introduced specific mutations into the Fgfr1 locus by gene targeting. Our results show functional dominance of one of the receptor isoforms and suggest a function for the autophosphorylation of site Y766 in the negative regulation of FGFR1 activity. Y766F and hypomorphic mutations in Fgfr1 generate opposite phenotypes in terms of homeotic vertebral transformations, suggesting a role for FGFR1 in patterning the embryonic anteriorposterior axis by way of regulation of Hox gene activity.     

Using neural networks to select wavelet features for breast cancerdiagnosis

This study focuses on improving microcalcification classification by establishing an efficient computer-aided diagnosis system that extracts Daubechies-4 and biorthogonal wavelet features. These wavelets were chosen because they have been used in military target recognition and fingerprint recognition research with images characterized by low contrast, similar to mammography. Feature selection techniques are employed to further increase classification performance. The artificial neural network feature selection techniques are complemented by a conventional decision boundary-based feature selection method. The results using the wavelet features are compared to more conventional measures of image texture, angular second moment, and Karhunen Loeve coefficients. The use of alternative signal processing to compare wavelet and neural techniques allows for a measure of the problem difficulty. It is concluded that advances and contributions have been made with the introduction of two novel feature extraction methods for breast cancer diagnosis, wavelets and eigenmasses. Additionally, feature selection techniques are demonstrated, compared, and validated, transforming adequate discrimination power into promising classification results     

High-resolution analysis of T-cell receptor beta-chain repertoires using DNA heteroduplex tracking: generally stable, clonal CD8+ expansions in all healthy young adults

Harringtonine (HT), an anticancer drug with high chemotherapeutic efficiency to human chronic granulocytic/myelomonocytic leukemia, has been reported to rapidly induce apoptosis in HL-60 cells in a wide scope/range of dosage by investigators from our lab and others. In the present studies, by using video enhancement contrast (VEC) microscopy, we dynamically analyzed changes in intracellular calcium distribution in a single HL-60 cell over the period from the initiation of apoptosis to the obvious appearance of chromatin condensation. The results from this paper demonstrated the striking distinction of intracellular calcium distribution at different time points after treatment with HT. Before treatment in normal HL-60 cells the highest [Ca2+]i accumulation was observed in the peri-nuclear area and the lowest was observed in the nucleus; after treatment with 1 microg/ml HT for 30 min intracellular calcium diffused all over the cell compartments, while intranuclear calcium increased comparatively and significantly. The phenomenon of intranuclear calcium accumulation was further confirmed by using laser scanning confocal microscopy (LSCM). In addition, co-localization of the highest calcium region with condensed chromatin in apoptotic HL-60 cells was also observed by LSCM. Our results suggest that two sequential alterations of intracellular calcium distribution occurred in apoptotic HL-60 cells induced by HT, i.e. (a) accumulation of calcium in the nucleus and (b) regionalization in a specific nuclear region.     

Uncertainty of discrete stochastic systems: general theory andstatistical inference

Uncertainty is defined in a new manner, as a function of discrete probability distributions satisfying a simple and intuitively appealing weak monotonicity condition. It is shown that every uncertainty is Schur-concave and conversely, every Schur-concave function of distributions is an uncertainty. General properties of uncertainties are systematically studied. Many characteristics of distributions introduced previously in statistical physics, mathematical statistics, econometrics and information theory are shown to be particular examples of uncertainties. New examples are introduced, and traditional as well as some new methods for obtaining uncertainties are discussed. The information defined by decrease of uncertainty resulting from an observation is investigated and related to previous concepts of information. Further, statistical inference about uncertainties is investigated, based on independent observations of system states. In particular, asymptotic distributions of maximum likelihood estimates of uncertainties and uncertainty-related functions are derived, and asymptotically α-level Neyman-Pearson tests of hypotheses about these system characteristics are presented     

Optimal structural damping of skis using a genetic algorithm

Two problems of the optimal damping of skis with the use of an improved genetic algorithm (GA) are presented. The first problem is the optimal location of viscoelastic constrained layers; the second problem is the optimization of the stacking sequence with or without variable layer thicknesses. It is demonstrated that the GA is a very attractive and powerful method for these problems in which the objective function has no derivatives and several optimal solutions simultaneously exist.     

Stochastic modelling of temperatures for a full-scale occupied building zone subject to natural random influences

Buildings are usually subject to a variety of stochastic influences. Though the deterministic approach to building thermal modelling is widespread, it cannot easily model the effects of such influences, and a different approach might be better. In this study, stochastic models are derived which describe the thermal behaviour of a full-scale room exposed to the naturally occurring disturbances of climate (temperature, solar irradiance, infiltration), occupancy and appliance usage. A Box-Jenkins time series analysis technique is employed, and univariate stochastic models are fitted to the internal and external air temperature series. The models are validated by comparing the observed temperature with values forecasted ahead (in steps of 1 h) by the models, over a 36-h period; agreement was found to be good. It is concluded that the stochastic modelling approach can be applied successfully to the thermal analysis of a building's behaviour, thereby affording a method which accounts for random influences in a compact model format. The technique has particular relevance to advanced model-based control implemented via ‘intelligent’ digital controllers and building energy management systems, and its application in this respect is discussed.