Forecast Cosmological Constraints with the 1D Wavelet Scattering Transform and the Lyman-α Forest.

We make forecasts for the constraining power of the 1D wavelet scattering transform when used with a Lyman-α forest cosmology survey. Using mock simulations and a Fisher matrix, we show that there is considerable cosmological information in the scattering transform coefficients not captured by the flux power spectrum. We estimate mock covariance matrices assuming uncorrelated Gaussian pixel noise for each quasar at a level drawn from a simple log-normal model. The extra information comes from a smaller estimated covariance in the first-order wavelet power and from second-order wavelet coefficients that probe non-Gaussian information in the forest. Forecast constraints on cosmological parameters from the wavelet scattering transform are more than an order of magnitude tighter than for the power spectrum, shrinking a 4D parameter space by a factor of 10^{6}. Should these improvements be realized with the Dark Energy Spectroscopic Instrument, inflationary running would be constrained to test common inflationary models predicting α_{s}=-6×10^{-4} and neutrino mass constraints would be improved enough for a 5-σ detection of the minimal neutrino mass.

Classification of breast ultrasound images using fractal feature.

Fractal analyses have been applied successfully for the image compression, texture analysis, and texture image segmentation. The fractal dimension could be used to quantify the texture information. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Furthermore, a computer-aided diagnosis (CAD) system based on the fractal analysis is proposed to classify the breast lesions into two classes: benign and malignant. To improve the classification performances, the ultrasound images are preprocessed by using morphology operations and histogram equalization. Finally, the k-means classification method is used to classify benign tumors from malignant ones. The US breast image databases include only histologically confirmed cases: 110 malignant and 140 benign tumors, which were recorded. All the digital images were obtained prior to biopsy using by an ATL HDI 3000 system. The receiver operator characteristic (ROC) area index AZ is 0.9218, which represents the diagnostic performance.

Jun dimerization protein 2 in oxygen restriction; control of senescence.

Senescent cells show a series of alterations, including a flat and enlarged morphology, increase in nonspecific acidic ß- galactosidase activity, chromatin condensation, and changes in gene expression patterns. The onset and maintenance of senescence are regulated by two tumor suppressor proteins, p53 and Rb, whose expression is controlled by two distinct proteins, p19(Arf) and p16(Ink4a), respectively, which are encoded by the cdkn2a locus. Transcription factor Jun dimerization protein 2 (JDP2) which binds directly to histones and DNA, inhibits the acetylation and methylation of core histones and of reconstituted nucleosomes that contain JDP2-recognition DNA sequences. JDP2-deficient mouse embryonic fibroblasts are known to be resistant to replicative senescence. Oxygen induces the expression of the JDP2 gene and JDP2 then inhibits the recruitment of polycomb repressive complexes (PRCs1 and 2) to the promoter of the gene encoding p16(Ink4a), resulting in the inhibition of methylation of lysine 27 of histone H3. These findings suggest that chromatin-remodeling factors, including the PRC complex controlled by JDP2, are important players in the senescence. The newly defined mechanisms that underlie the action of oxygen in the induction of JDP2 and cellular senescence are reviewed.