Relationship between Atomic Structure, Composition, and Dielectric Constant in Zr-SiO2 Glasses.

Computational methods, or computer-aided material design (CAMD), used for the analysis and design of materials have a relatively long history. However, the applicability of CAMD has been limited by the scales of computational resources generally available in the past. The surge in computational power seen in recent years is enabling the applicability of CAMD to unprecedented levels. Here, we focus on the CAMD for materials critical for the continued advancement of the complementary metal oxide semiconductor (CMOS) semiconductor technology. In particular, we apply CAMD to the engineering of high-permittivity dielectric materials. We developed a Reax forcefield that includes Si, O, Zr, and H. We used this forcefield in a series of simulations to compute the static dielectric constant of silica glasses for low Zr concentration using a classical molecular dynamics approach. Our results are compared against experimental values. Not only does our work reveal numerical estimations on ZrO2-doped silica dielectrics, it also provides a foundation and demonstration of how CAMD can enable the engineering of materials of critical importance for advanced CMOS technology nodes.

Oncogene-Expressing Senescent Melanocytes Up-Regulate MHC Class II, a Candidate Melanoma Suppressor Function.

On acquisition of an oncogenic mutation, primary human and mouse cells can enter oncogene-induced senescence (OIS). OIS is characterized by a stable proliferation arrest and secretion of proinflammatory cytokines and chemokines, the senescence-associated secretory phenotype. Proliferation arrest and the senescence-associated secretory phenotype collaborate to enact tumor suppression, the former by blocking cell proliferation and the latter by recruiting immune cells to clear damaged cells. However, the interactions of OIS cells with the immune system are still poorly defined. Here, we show that engagement of OIS in primary human melanocytes, specifically by melanoma driver mutations NRASQ61K and BRAFV600E, causes expression of the major histocompatibility class II antigen presentation apparatus, via secreted IL-1ß signaling and expression of CIITA, a master regulator of major histocompatibility class II gene transcription. In vitro, OIS melanocytes activate T-cell proliferation. In vivo, nonproliferating oncogene-expressing melanocytes localize to skin-draining lymph nodes, where they induce T-cell proliferation and an antigen presentation gene expression signature. In patients, expression of major histocompatibility class II in melanoma is linked to favorable disease outcome. We propose that OIS in melanocytes is accompanied by an antigen presentation phenotype, likely to promote tumor suppression via activation of the adaptive immune system.

HIRA orchestrates a dynamic chromatin landscape in senescence and is required for suppression of neoplasia.

Cellular senescence is a stable proliferation arrest that suppresses tumorigenesis. Cellular senescence and associated tumor suppression depend on control of chromatin. Histone chaperone HIRA deposits variant histone H3.3 and histone H4 into chromatin in a DNA replication-independent manner. Appropriately for a DNA replication-independent chaperone, HIRA is involved in control of chromatin in nonproliferating senescent cells, although its role is poorly defined. Here, we show that nonproliferating senescent cells express and incorporate histone H3.3 and other canonical core histones into a dynamic chromatin landscape. Expression of canonical histones is linked to alternative mRNA splicing to eliminate signals that confer mRNA instability in nonproliferating cells. Deposition of newly synthesized histones H3.3 and H4 into chromatin of senescent cells depends on HIRA. HIRA and newly deposited H3.3 colocalize at promoters of expressed genes, partially redistributing between proliferating and senescent cells to parallel changes in expression. In senescent cells, but not proliferating cells, promoters of active genes are exceptionally enriched in H4K16ac, and HIRA is required for retention of H4K16ac. HIRA is also required for retention of H4K16ac in vivo and suppression of oncogene-induced neoplasia. These results show that HIRA controls a specialized, dynamic H4K16ac-decorated chromatin landscape in senescent cells and enforces tumor suppression.

Lysosome-mediated processing of chromatin in senescence.

Cellular senescence is a stable proliferation arrest, a potent tumor suppressor mechanism, and a likely contributor to tissue aging. Cellular senescence involves extensive cellular remodeling, including of chromatin structure. Autophagy and lysosomes are important for recycling of cellular constituents and cell remodeling. Here we show that an autophagy/lysosomal pathway processes chromatin in senescent cells. In senescent cells, lamin A/C-negative, but strongly γ-H2AX-positive and H3K27me3-positive, cytoplasmic chromatin fragments (CCFs) budded off nuclei, and this was associated with lamin B1 down-regulation and the loss of nuclear envelope integrity. In the cytoplasm, CCFs were targeted by the autophagy machinery. Senescent cells exhibited markers of lysosomal-mediated proteolytic processing of histones and were progressively depleted of total histone content in a lysosome-dependent manner. In vivo, depletion of histones correlated with nevus maturation, an established histopathologic parameter associated with proliferation arrest and clinical benignancy. We conclude that senescent cells process their chromatin via an autophagy/lysosomal pathway and that this might contribute to stability of senescence and tumor suppression.

A small-window moving average-based fully automated baseline estimation method for Raman spectra.

A fully automated and model-free baseline-correction method for vibrational spectra is presented. It iteratively applies a small, but increasing, moving average window in conjunction with peak stripping to estimate spectral baselines. Peak stripping causes the area stripped from the spectrum to initially increase and then diminish as peak stripping proceeds to completion; a subsequent increase is generally indicative of the commencement of baseline stripping. Consequently, this local minimum is used as a stopping criterion. A backup is provided by a second stopping criterion based on the area under a third-order polynomial fitted to the first derivative of the current estimate of the baseline-free spectrum and also indicates whether baseline is being stripped. When the second stopping criterion is triggered instead of the first one, a proportionally scaled simulated Gaussian baseline is added to the current estimate of the baseline-free spectrum to act as an internal standard to facilitate subsequent processing and termination via the first stopping criterion. The method is conceptually simple, easy to implement, and fully automated. Good and consistent results were obtained on simulated and real Raman spectra, making it suitable for the fully automated baseline correction of large numbers of spectra.

Noise reduction methods applied to two-dimensional correlation spectroscopy (2D-COS) reveal complementary benefits of pre- and post-treatment.

Two-dimensional correlation spectroscopy (2D-COS) is a powerful spectral analysis technique widely used in many fields of spectroscopy because it can reveal spectral information in complex systems that is not readily evident in the original spectral data alone. However, noise may severely distort the information and thus limit the technique's usefulness. Consequently, noise reduction is often performed before implementing 2D-COS. In general, this is implemented using one-dimensional (1D) methods applied to the individual input spectra, but, because 2D-COS is based on sets of successive spectra and produces 2D outputs, there is also scope for the utilization of 2D noise-reduction methods. Furthermore, 2D noise reduction can be applied either to the original set of spectra before performing 2D-COS ("pretreatment") or on the 2D-COS output ("post-treatment"). Very little work has been done on post-treatment; hence, the relative advantages of these two approaches are unclear. In this work we compare the noise-reduction performance on 2D-COS of pretreatment and post-treatment using 1D (wavelets) and 2D algorithms (wavelets, matrix maximum entropy). The 2D methods generally outperformed the 1D method in pretreatment noise reduction. 2D post-treatment in some cases was superior to pretreatment and, unexpectedly, also provided correlation coefficient maps that were similar to 2D correlation spectroscopy maps but with apparent better contrast.

A model-free, fully automated baseline-removal method for Raman spectra.

We present here a fully automated spectral baseline-removal procedure. The method uses a large-window moving average to estimate the baseline; thus, it is a model-free approach with a peak-stripping method to remove spectral peaks. After processing, the baseline-corrected spectrum should yield a flat baseline and this endpoint can be verified with the χ(2)-statistic. The approach provides for multiple passes or iterations, based on a given χ(2)-statistic for convergence. If the baseline is acceptably flat given the χ(2)-statistic after the first pass at correction, the problem is solved. If not, the non-flat baseline (i.e., after the first effort or first pass at correction) should provide an indication of where the first pass caused too much or too little baseline to be subtracted. The second pass thus permits one to compensate for the errors incurred on the first pass. Thus, one can use a very large window so as to avoid affecting spectral peaks--even if the window is so large that the baseline is inaccurately removed--because baseline-correction errors can be assessed and compensated for on subsequent passes. We start with the largest possible window and gradually reduce it until acceptable baseline correction based on the χ(2) statistic is achieved. Results, obtained on both simulated and measured Raman data, are presented and discussed.

A matrix-based two-dimensional regularization algorithm for signal-to-noise ratio enhancement of multidimensional spectral data.

We present a new spectral image processing algorithm, the "matrix maximum entropy method" (MxMEM), which offers efficient signal-to-noise ratio (SNR) enhancement of multidimensional spectral data. MxMEM is based upon two previous regularization methods that employ the maximum entropy concept. The first is a one-dimensional (1D) algorithm, which smoothes individual vectors, called the two-point maximum entropy method (TPMEM). The second is a two-dimensional (2D) form called 2D TPMEM, that smoothes images but processes them one vector at a time. MxMEM is a truly two dimensional image processing algorithm in that its "smoothing engine" performs two-dimensional processing in every iteration. We demonstrate that this matrix-based construction makes more effective use of two-dimensionally embedded information and thus confers significant advantages over other regularization approaches. In addition, we utilize the concept that individual related Raman spectra can be combined in a matrix to form an artificial Raman "image". We show that, when processed as an image, superior SNR enhancement is achieved compared to processing the same data by TPMEM one spectrum at a time.

Fully automated high-performance signal-to-noise ratio enhancement based on an iterative three-point zero-order Savitzky-Golay filter.

The automated processing of data from high-throughput and real-time collection procedures is becoming a pressing problem. Currently the focus is shifting to automated smoothing techniques where, unlike background subtraction techniques, very few methods exist. We have developed a filter based on the widely used and conceptually simple moving average method or zero-order Savitzky-Golay filter and its iterative relative, the Kolmogorov-Zurbenko filter. A crucial difference, however, between these filters and our implementation is that our fully automated smoothing filter requires no parameter specification or parameter optimization. Results are comparable to, or better than, Savitzky-Golay filters with optimized parameters and superior to the automated iterative median filter. Our approach, because it is based on the highly familiar moving average concept, is intuitive, fast, and straightforward to implement and should therefore be of immediate and considerable practical use in a wide variety of spectroscopy applications.

Chi-squared-based filters for high-fidelity signal-to-noise ratio enhancement of spectra.

When reconstructing a measured spectrum to enhance its signal-to-noise ratio (SNR), the objective is to minimize the variance between the smooth reconstructed spectrum and the original measured spectrum, hence to attain an acceptably small chi2 value. The chi2 value thus measures the fidelity of the reconstruction to the original. Smoothness can be conceived as attenuated variation between adjacent points in a spectrum. Thus, a conceptual change in the application of the chi2 function to the difference between adjacent points of the reconstructed spectrum permits its use, in principle, as both a measure of smoothness and a measure of fidelity. We show here that implementations of this concept produce results superior to Savitzky-Golay filters.

Two-point maximum entropy noise discrimination in spectra over a range of baseline offsets and signal-to-noise ratios.

The two-point maximum entropy method (TPMEM) is a useful method for signal-to-noise ratio enhancement and deconvolution of spectra, but its efficacy is limited under conditions of high background offsets. This means that spectra with high average background levels, regions with high background in spectra with varying background levels, and regions of high signal-to-noise ratios are smoothed less effectively than spectra or spectral regions without these conditions. We report here on the cause of this TPMEM limitation and on appropriate baseline estimation and removal procedures that effectively minimize the effects on regularization. We also present a comparative analysis of TPMEM and Savitzky-Golay filtering to facilitate selection of the best technique under a given range of conditions.