Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation. The last 10-15 years have seen the development of many new high-resolution decompositions that are often fundamentally different from Fourier and wavelet transforms. These conventional techniques, like the short-time Fourier transform and the continuous wavelet transform, show some limitations in terms of resolution (localization) due to the trade-off between time and frequency localizations and smearing due to the finite size of the time series of their template. Well-known techniques, like autoregressive methods and basis pursuit, and recently developed techniques, such as empirical mode decomposition and the synchrosqueezing transform, can achieve higher time-frequency localization due to reduced spectral smearing and leakage. We first review the theory of various established and novel techniques, pointing out their assumptions, adaptability, and expected time-frequency localization. We illustrate their performances on a provided collection of benchmark signals, including a laughing voice, a volcano tremor, a microseismic event, and a global earthquake, with the intention to provide a fair comparison of the pros and cons of each method. Finally, their outcomes are discussed and possible avenues for improvements are proposed.

Introduction Notch is a family of transmembrane protein receptors whose activation requires proteolytic cleavage by γ-secretase. Since aberrant Notch signaling can induce mammary carcinomas in transgenic mice and high expression levels of Notch receptors and ligands correlates with overall poor clinical outcomes, inhibiting γ-secretase with small molecules may be a promising approach for breast cancer treatment. Consistent with this hypothesis, two recent papers reported that γ-secretase inhibitor I (GSI I), Z-LLNle-CHO, is toxic to breast cancer cells both in vitro and in vivo. In this study, we compared the activity and cytotoxicity of Z-LLNle-CHO to that of two highly specific GSIs, DAPT and L-685,458 and three structurally unrelated proteasome inhibitors, MG132, lactacystin, and bortezomib in order to study the mechanism underlying the cytotoxicity of Z-LLNle-CHO in breast cancer cells.

Time-frequency analysis plays a significant role in seismic data processing and interpretation. Complete ensemble empirical mode decomposition decomposes a seismic signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. Analysis on synthetic and real data demonstrates that this method promises higher spectral-spatial resolution than the short-time Fourier transform or wavelet transform. Application on field data thus offers the potential of highlighting subtle geologic structures that might otherwise escape unnoticed.

Time-frequency representation of seismic signals provides a source of information that is usually hidden in the Fourier spectrum. The short-time Fourier transform and the wavelet transform are the principal approaches to simultaneously decompose a signal into time and frequency components. Known limitations, such as trade-offs between time and frequency resolution, may be overcome by alternative techniques that extract instantaneous modal components. Empirical mode decomposition aims to decompose a signal into components that are well separated in the time-frequency plane allowing the reconstruction of these components. On the other hand, a recently proposed method called the “synchrosqueezing transform” (SST) is an extension of the wavelet transform incorporating elements of empirical mode decomposition and frequency reassignment techniques. This new tool produces a well-defined time-frequency representation allowing the identification of instantaneous frequencies in seismic signals to highlight individual components. We introduce the SST with applications for seismic signals and produced promising results on synthetic and field data examples.

The thymus is an intricate primary lymphoid organ, wherein bone marrow–derived lymphoid progenitor cells are induced to develop into functionally competent T cells that express a diverse TCR repertoire, which is selected to allow for the recognition of foreign Ags while avoiding self-reactivity or autoimmunity. Thymus stromal cells, which can include all non–T lineage cells, such as thymic epithelial cells, endothelial cells, mesenchymal/fibroblast cells, dendritic cells, and B cells, provide signals that are essential for thymocyte development as well as for the homeostasis of the thymic stroma itself. In this brief review, we focus on the key roles played by thymic stromal cells during early stages of T cell development, such as promoting the homing of thymic-seeding progenitors, inducing T lineage differentiation, and supporting thymocyte survival and proliferation. We also discuss recent advances on the transcriptional regulation that govern thymic epithelial cell function as well as the cellular and molecular changes that are associated with thymic involution and regeneration.