No abstract

The absorption and fluorescence spectra and second harmonic generation (SHG) of the insoluble monolayer of bis-(N-ethyl,N-octadecyl)rhodamine (RhC18) at the air−water interface have been measured. These spectra were affected significantly by compression, and the observed changes were ascribed to the formation and structural rearrangement of aggregated species on the water surface during compression. The spectroscopic behavior of the monolayer was explained in accordance with its rheological properties, and the transition from disordered monomers to dimers, from dimers to aggregates, and from aggregates to two-dimensional arrays was proposed. SHG studies revealed that the RhC18 molecules in the expanded film region are oriented with their C 2-axis tilted away from the surface normal on angle θ distributed in the range of 31−39°. The rotational distribution around the C 2-axis was assumed to be 45−60° according to preferable intermolecular interactions with the water subphase and surrounding molecules. The θ angle distribution became slightly narrow because of the increase of molecular ordering caused by two-dimensional external pressure. The sharp increase of SHG intensity and the phase shift observed at high compression were ascribed to the formation of blue-shifted aggregates with their electronic transition being in resonance with the incident laser frequency. The results of spectroscopic and SHG studies were jointly analyzed, and the structural rearrangement within the monolayer during compression was described.

The tumor microenvironment offers favorable conditions for tumor progression, and activated fibroblasts, known as cancer‐associated fibroblasts, play a pivotal role. TP53‐deficient cancer cells are known to induce strong fibroblast activation. We aimed to elucidate the oncogenic role of exosomes derived from TP53‐deficient colon cancer cells in fibroblast proliferation and tumor growth. Cancer cell‐derived exosomes (CDEs) were isolated from the conditioned media of cancer cells using a sequential ultracentrifugation method. The effects of exosomes on tumor growth were evaluated using human cell lines (TP53‐WT colon cancer, HCT116; TP53‐mutant colon cancer, HT29; and fibroblasts, CCD‐18Co and WI‐38) and an immune‐deficient nude mouse xenograft model. HCT116 (HCT116sh p53) cells deficient in TP53 accelerated cocultured fibroblast proliferation compared to TP53‐WT HCT116 (HCT116sh control) cells in vitro. Exosomes from HCT116sh p53 cells suppressed TP53 expression of fibroblasts and promoted their proliferation. Xenografts of HCT116sh p53 cells grew significantly faster than those of HCT116sh control cells in the presence of co‐injected fibroblasts, but this difference was diminished by CDE inhibition. Microarray analysis identified upregulation of several microRNAs (miR‐1249‐5p, miR‐6737‐5p, and miR‐6819‐5p) in TP53‐deficient CDEs, which were functionally proven to suppress TP53 expression in fibroblasts. Exosomes derived from TP53‐mutant HT29 cells also suppressed TP53 expression in fibroblasts and accelerated their growth. The proliferative effect of HT29 on cocultured fibroblasts was diminished by inhibition of these miRNAs in fibroblasts. Our results suggest that CDEs play a pivotal role in tumor progression by fibroblast modification. Cancer cell‐derived exosomes might, therefore, represent a novel therapeutic target in colon cancer.

Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. Nonlinear impairments are determined by the signal pattern and the transmission system parameters. Deterministic algorithms based on approximating the nonlinear Schrodinger equation through digital back propagation, or a single step approach based on perturbation methods have been demonstrated, however, their implementation demands excessive signal processing resources, and accurate knowledge of the transmission system. A completely different approach uses machine learning algorithms to learn from the received data itself to figure out the nonlinear impairment. In this work, a single-step, system agnostic nonlinearity compensation algorithm based on a neural network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6 dB Q improvement after 2800 km standard single-mode fiber transmission using 32 Gbaud signal. Without prior knowledge of the transmission system, the neural network tensor weights are constructed from training data thanks to the intra-channel cross-phase modulation and intra-channel four-wave mixing triplets used as input features.