Wnt/ß-catenin activation and macrophage induction during liver cancer development following steatosis.

Obesity confers an independent risk for carcinogenesis. In the liver, steatosis often proceeds cancer formation; however, the mechanisms by which steatosis promotes carcinogenesis is unknown. We hypothesize that steatosis alters the microenvironment to promote proliferation of tumor initiating cells (TICs) and carcinogenesis. We used several liver cancer models to address the mechanisms underlying the role of obesity in cancer and verified these findings in patient populations. Using bioinformatics analysis and verified by biochemical assays, we identified that hepatosteatosis resulting from either Pten deletion or transgenic expression of HCV core/NS5A proteins, promotes the activation of Wnt/ß-catenin. We verified that high fat diet lipid accumulation is also capable of inducing Wnt/ß-catenin. Caloric restriction inhibits hepatosteatosis, reduces Wnt/ß-catenin activation and blocks the expansion of TICs leading to complete inhibition of tumorigenesis without affecting the phosphatase and tensin homologue deleted on chromosome 10 (PTEN) loss regulated protein kinase B (AKT) activation. Pharmacological inhibition or loss of the Wnt/ß-catenin signal represses TIC growth in vitro, and decreases the accumulation of TICs in vivo. In human liver cancers, ontology analysis of gene set enrichment analysis (GSEA)-defined Wnt signature genes indicates that Wnt signaling is significantly induced in tumor samples compared with healthy livers. Indeed, Wnt signature genes predict 90% of tumors in a cohort of 558 patient samples. Selective depletion of macrophages leads to reduction of Wnt and suppresses tumor development, suggesting infiltrating macrophages as a key source for steatosis-induced Wnt expression. These data established Wnt/ß-catenin as a novel signal produced by infiltrating macrophages induced by steatosis that promotes growth of tumor progenitor cells, underlying the increased risk of liver tumor development in obese individuals.

Spectral properties of Titan's impact craters imply chemical weathering of its surface.

We examined the spectral properties of a selection of Titan's impact craters that represent a range of degradation states. The most degraded craters have rims and ejecta blankets with spectral characteristics that suggest that they are more enriched in water ice than the rims and ejecta blankets of the freshest craters on Titan. The progression is consistent with the chemical weathering of Titan's surface. We propose an evolutionary sequence such that Titan's craters expose an intimate mixture of water ice and organic materials, and chemical weathering by methane rainfall removes the soluble organic materials, leaving the insoluble organics and water ice behind. These observations support the idea that fluvial processes are active in Titan's equatorial regions.

Complete memory structures for approximating nonlinear discrete-time mappings.

This paper introduces a general structure that is capable of approximating input-output maps of nonlinear discrete-time systems. The structure is comprised of two stages, a dynamical stage followed by a memoryless nonlinear stage. A theorem is presented which gives a simple necessary and sufficient condition for a large set of structures of this form to be capable of modeling a wide class of nonlinear discrete time systems. In particular, we introduce the concept of a "complete memory". A structure with a complete memory dynamical stage and a sufficiently powerful memoryless stage is shown to be capable of approximating arbitrarily wide class of continuous, causal, time invariant, approximately-finite-memory mappings between discrete-time signal spaces. Furthermore, we show that any bounded-input bounded output, time-invariant, causal memory structure has such an approximation capability if and only if it is a complete memory. Several examples of linear and nonlinear complete memories are presented. The proposed complete memory structure provides a template for designing a wide variety of artificial neural networks for nonlinear spatiotemporal processing.