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• Understood the role of genome regulation at the biological level by defining the overall profile of chromosomal organization. • HNRNPU plays important role in 3D genome organization by high-throughput assays. • The disruption of this protein not only results in compartment switching on of the genome. The complexity of higher eukaryote genomes is far from being explained by linear information. There is a need to understand roles of genome regulation at the organism level through defining a comprehensive profile of chromosomal organization. Chromosome conformation capture (3C)-based studies reveal that higher-order of chromatin include not only long-range chromatin loops, but also compartments and topologically associating domains as the basis of genome structure and functions. However, the molecular machinery how the genome is spatially organized is still inadequate. Exciting progress has been made with the development of today's technology, we find that heterogeneous nuclear ribonucleoprotein U, initially identified as a structural nuclear protein, plays important role in three-dimensional (3D) genome organization by high-throughput assays. The disruption of this protein not only results in compartment switching on of the genome, it also reduces of TAD boundary strengths at borders between two types of compartments, and regulates chromatin loop by decrease its intensities. In addition, HNRNPU mainly binds to active chromatin. Most of HNRNPU peaks is consistent with CTCF or RAD21.It also plays an irreplaceable role in the processes of mitosis. This review aims to discuss the role of HNRNPU in maintaining the 3D chromatin architecture, as well as the recent development and human diseases involved in this nuclear matrix (NM)-associated protein. [ABSTRACT FROM AUTHOR]

• A full generalization of Gini index is proposed for sparse quantification. • Fully generalized Gini indices are theoretically proven to be sparsity measures. • Two key performances of fully generalized Gini indices are investigated. • Fully generalized Gini indices exhibit excellent performance in characterizing repetitive transients. • Fully generalized Gini indices can effectively achieve bearing condition monitoring and fault diagnosis. The classic Gini index (GI) is generalized recently by using nonlinear weight sequences as sparsity measures for sparse quantification and machine condition monitoring. The generalized GIs with different weight parameters are more robust to random transients. However, they show insufficient performance in discriminating repetitive transients under noise contamination. To overcome this shortage, this paper proposes a two-parameter generalization method to tune not only the weight parameter but also the norm order, allowing for a full generalization of the classic GI to quantify transient features and leading to new statistical indicators which are named fully generalized GIs (FGGIs). Mathematical derivations show that FGGIs satisfy at least four of the six typical attributes of sparsity measures and that those with weight parameter equal to one satisfy at least five sparse attributes, proving that they are a new family of sparsity measures. Numerical simulations demonstrate that FGGIs can monotonically evaluate the sparseness of the signals and that the FGGIs with appropriate parameters exhibit improved performance in resisting random transient interferences and discriminating noise-contaminated repetitive transients compared to traditional sparsity measures. The performance of FGGIs in the condition monitoring of rolling element bearings is validated using two different run-to-failure experiment datasets, including a gradual failure and a sudden failure. The results show that increasing the norm order can improve the capability of FGGIs to characterize transient fault features, allowing more accurate trending of bearing health conditions, and therefore achieving better condition monitoring performance than the traditional sparsity measures. [ABSTRACT FROM AUTHOR]

Targeted therapies are suggested as an effective alternative for patients with cancer that harbor mutations, but treatment outcomes are frequently limited by primary or acquired drug resistance. The present review describes potential mechanisms of primary or acquired drug resistances to provide a resource for considering how to be overcome. We focus on strategies of targeted drug combinations to minimize the development of drug resistance within the context how resistance develops. Strategies benefit from the combined use of “omics” technologies, i.e., high-throughput functional genomics data, pharmacogenomics, or genome-wide CRISPR-Cas9 screening, to analyze and design targeted drug combinations for mutation-driven drug resistance. We also introduce new insights towards pathway-centric combined therapies as an alternative to overcome the heterogeneity and benefit patient prognoses. [ABSTRACT FROM AUTHOR]

• Generalized envelopes and generalized envelope spectra are proposed for signal demodulation. • The performance of generalized envelope spectra in rolling bearing fault diagnosis is revealed using simulations and experiments. • The product envelope spectrum is proposed as an enhanced spectral analysis tool. • Product Envelope Spectrum Optimization-gram (PESOgram) is developed for rolling bearing fault diagnosis. • PESOgram shows excellent capability in identifying bearing fault characteristic frequencies. The vibration signal of a faulty rolling bearing exhibits typical non-stationarity – often in the form of cyclostationarity. The spectrum tools often used to characterize cyclostationarity mainly include envelope spectrum, squared envelope spectrum and log-envelope spectrum. In this paper, new detection methods of cyclostationarity are developed for obtaining a larger family of envelope analysis and their effectiveness in rolling bearing fault diagnosis is evaluated rigorously. Firstly, based on the simplified Box-Cox transformation, the generalized envelope signals are constructed from the analytic signal for demodulation purposes, and then a spectrum family named generalized envelope spectra (GESs) is proposed to reveal cyclostationarity. Especially, GESs with different transformation parameters exhibit different performance advantages against the random impulse noise and Gaussian background noise which are commonly present in rolling bearing vibration signals. Subsequently, a novel spectrum tool that combines the performance advantages of different GESs, called product envelope spectrum (PES), is developed to strengthen the capability to detect cyclostationarity. Finally, an enhanced envelope analysis named Product Envelope Spectral Optimization-gram (PESOgram) is proposed to improve the accuracy and robustness of PES for rolling bearing fault diagnosis in the presence of different fault-unrelated interference noises. The performance of the PESOgram method is validated on numerically generated signal and experimental signals collected from two railway axle bearing test rigs and compared with several state-of-the-art envelope analysis methods. The results demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings and its advantages over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]