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Ferroptosis is a form of regulated cell death that can be modulated by small molecules and has the potential for the development of therapeutics for oncology. Although excessive lipid peroxidation is the defining hallmark of ferroptosis, DNA damage may also play a significant role. In this study, a potential mechanistic role for MIF in homologous recombination (HR) DNA repair is identified. The inhibition or genetic depletion of MIF or other HR proteins, such as breast cancer type 1 susceptibility protein (BRCA1), is demonstrated to significantly enhance the sensitivity of cells to ferroptosis. The interference with HR results in the translocation of the tumor suppressor protein p53 to the mitochondria, which in turn stimulates the production of reactive oxygen species. Taken together, the findings demonstrate that MIF‐directed small molecules enhance ferroptosis via a putative MIF‐BRCA1‐RAD51 axis in HR, which causes resistance to ferroptosis. This suggests a potential novel druggable route to enhance ferroptosis by targeted anticancer therapeutics in the future. [ABSTRACT FROM AUTHOR]

Traditional beamforming performance will significantly degrade with insufficient training samples on multi-dimensional arrays with much higher dimensions. In this paper, a tensor beamforming method that has advantages in the number of training snapshots required is introduced based on the proposed tensor model and the separability of sub-dimensions for multi-dimensional arrays. Then, for coherent interference, based on the model of the sub-dimension's interference covariance matrix in tensor beamforming, a robust tensor beamforming method is proposed by directly estimating the interference covariance matrix. Analysis shows that the proposed method could overcome the non-homogeneous clutter environment and coherent interference and obtain a higher output signal-to-interference-noise ratio. Taking the given two-dimensional polarization-sensitive array as an example, the number of training snaps required by the proposed method is 1/3 of the traditional method, and the output signal-to-interference-to-noise ratio increases by about 2.5 dB under coherent interference scenario. The simulation results verify the effectiveness of the analysis. [ABSTRACT FROM AUTHOR]

The identification of a blood vessel's elastic properties by an ultrasonic guided wave mainly depends on the accurate propagation characteristics, which are obtained by solving the problem of elastic mechanics based on a thin-plate model. However, this method cannot accurately predict the characteristics for low frequencies. Since blood vessels are of a tubular structure, a hollow-cylinder model, constructed to model blood vessels, is proposed in this paper. Based on this model, the propagation characteristics and dispersion curves of the ultrasonic guided wave propagating along the axial direction are studied by expanding the state equation using Legendre polynomials. A detailed comparison between the results of the proposed model and the thin-layer-based model is presented. It is shown that the dispersion curves of the L (0,1) modes, calculated by the two different models, are a match for high frequencies but differ for low frequencies. The dispersion curve of the L (0,1) mode calculated by the proposed model is in good agreement with the results of the reported experiments. Then, the relationship between the propagation characteristics of ultrasonic guided waves and Young's modulus is studied. It is discovered that the phase velocity and group velocity are significantly affected by Young's modulus close to the cutoff frequency, which has important implications for the selection of the detection frequency to the characteristic parameter of vascular. [ABSTRACT FROM AUTHOR]

We propose a method for blind multiuser detection (MUD) in synchronous systems over flat and fast Rayleigh fading channels. We adopt an autoregressive-moving-average (ARMA) process to model the temporal correlation of the channels. Based on the ARMA process, we propose a novel time-observation state-space model (TOSSM) that describes the dynamics of the addressed multiuser system. The TOSSM allows an MUD with natural blending of low-complexity particle filtering (PF) and mixture Kalman filtering (for channel estimation). We further propose to use a more efficient PF algorithm known as the stochastic M -algorithm (SMA), which, although having lower complexity than the generic PF implementation, maintains comparable performance.

Abstract Background About 6 million Americans suffer from heart failure and 70% of heart failure cases are caused by myocardial infarction (MI). Following myocardial infarction, increased cytokines induce two major types of macrophages: classically activated macrophages which contribute to extracellular matrix destruction and alternatively activated macrophages which contribute to extracellular matrix construction. Though experimental results have shown the transitions between these two types of macrophages, little is known about the dynamic progression of macrophages activation. Therefore, the objective of this study is to analyze macrophage activation patterns post-MI. Results We have collected experimental data from adult C57 mice and built a framework to represent the regulatory relationships among cytokines and macrophages. A set of differential equations were established to characterize the regulatory relationships for macrophage activation in the left ventricle post-MI based on the physical chemistry laws. We further validated the mathematical model by comparing our computational results with experimental results reported in the literature. By applying Lyaponuv stability analysis, the established mathematical model demonstrated global stability in homeostasis situation and bounded response to myocardial infarction. Conclusions We have established and validated a mathematical model for macrophage activation post-MI. The stability analysis provided a possible strategy to intervene the balance of classically and alternatively activated macrophages in this study. The results will lay a strong foundation to understand the mechanisms of left ventricular remodelling post-MI.

A novel inverter-output filter, which cannot only filter the differential-mode voltage dv/dt but also suppress the common-mode voltage dv/dt and their rms values, is proposed in this paper. The filter is in combination with a conventional RLC filter and a common-mode transformer. The main advantage is that the functions of filtering a differential-mode voltage and suppressing a common-mode voltage can be integrated into a single system. Furthermore, the structure and design of the proposed filter are rather simple because only passive components are used. Simulations and experiments are conducted to validate the performance of the proposed filter. Both of their results indicate that about 80% of the rms value of the common-mode voltage are suppressed, while the demand of differential-mode voltage filtering is still met. Index Terms--Common-mode voltage, differential-mode voltage, inverter, passive filter.

Abstract Background Transcriptional regulation by transcription factor (TF) controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task. Results We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method. Conclusions The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data.