Publications

Glioblastoma contains a hierarchy of stem-like cancer cells, but how this hierarchy is established is unclear. Here, we show that asymmetric Numb localization specifies glioblastoma stem-like cell (GSC) fate in a manner that does not require Notch inhibition. Numb is asymmetrically localized to CD133-hi GSCs. The predominant Numb isoform, Numb4, decreases Notch and promotes a CD133-hi, radial glial-like phenotype. However, upregulation of a novel Numb isoform, Numb4 delta 7 (Numb4d7), increases Notch and AKT activation while nevertheless maintaining CD133-hi fate specification. Numb knockdown increases Notch and promotes growth while favoring a CD133-lo, glial progenitor-like phenotype. We report the novel finding that Numb4 (but not Numb4d7) promotes SCF(Fbw7) ubiquitin ligase assembly and activation to increase Notch degradation. However, both Numb isoforms decrease epidermal growth factor receptor (EGFR) expression, thereby regulating GSC fate. Small molecule inhibition of EGFR activity phenocopies the effect of Numb on CD133 and Pax6. Clinically, homozygous NUMB deletions and low Numb mRNA expression occur primarily in a subgroup of proneural glioblastomas. Higher Numb expression is found in classical and mesenchymal glioblastomas and correlates with decreased survival. Thus, decreased Numb promotes glioblastoma growth, but the remaining Numb establishes a phenotypically diverse stem-like cell hierarchy that increases tumor aggressiveness and therapeutic resistance.

A combination of the sample entropy and the artificial intelligence (AI) would hopefully bring progress to the smart thunderstorm detection. In this article, we establish an entropy-based thunderstorm point charge imaging system with data clustering, in which data are ultrareliable and have low-latency 3-D atmospheric electric field (3DAEF) values. In particular, a high-resolution 3DAEF sensor with the single-axis rotary vane is developed to measure the time sequence signal of the 3DAEF. The signal is first denoised and then decomposed into multiple groups of branch data with the same number of samples. In order to break through the limitations of existing clustering rules, we propose a 3DAEF signal reconstruction method based on entropy intervals. By reconstructing the branch data, multiple time-scale data are formed. Finally, the curve fitting of the data is performed to realize the imaging after clustering the data. Experimental results suggest that the imaging error after the clustering is reduced by about 2.33%. Comparisons with radar charts show that the proposed system can effectively image the point charge moving paths.

We present a theoretical calculation of the dependence of reflectivity R pp of the improved fully leaky waveguide geometry, which comprises pyramid, matching fluid, and strongly anchored hybrid-aligned nematic liquid crystal (NLC) cell on the internal angle.The calculation is based on the multi-layer optical theory and the elastic theory of liquid crystals.For different sums of flexoelectric coefficients e 11 and e 33 , the curve of R pp moves a distance to the left or the right relative to the case of ignoring the flexoelectric effect and the distance of the movement varies with different flexoelectric coefficients.Consequently, the sum of flexoelectric coefficients can be explored by measuring the distance of the movement.

Cullin-RING ubiquitin ligases promote the polyubiquitination and degradation of many important cellular proteins, which previous studies indicated can be targeted for degradation via interaction with BTB domain-containing subunits of this E3 ligase complex. PEST domains are known to promote the degradation of proteins that contain them. However, the molecular mechanism by which PEST sequences promote degradation of these proteins is not understood. Here we show that the PEST sequences of a short-lived protein called HSF2 interact with Cullin3, a subunit of a Cullin-RING E3 ubiquitin ligase, and that this interaction mediates the Cul3-dependent ubiquitination and degradation of HSF2. These results indicate how, at the molecular level, PEST sequences can promote the proteolysis of proteins that contain them. They also expand understanding of the mechanisms by which substrates can be recruited to Cullin-RING E3 ubiquitin ligases to include interactions between PEST sequences and Cul3.

In this study, detection of small target in chaotic clutter with unknown dynamics is presented. We achieve this in four steps: (i) by using db3 wavelet decomposition of the signals, (ii) using Takens delay embedding theorem and least-squares support vector machine (LS-SVM) prediction, including increase the symmetric constraint and improve the kernel function, (iii) wavelet reconstruction, (iv) separation the weak signals from the prediction error. Efficiency of the new approach is evaluated by computing the root mean square error (RMSE) and signal-noise-radio (SNR) of the estimation. Lorenz attractor and the data from the McMaster IPIX radar sea clutter database will be used in the simulation. It is demonstrated in the simulation that compared with conventional RBF neural network LS-SVM regression prediction method; this approach has stronger generalization ability and better accuracy.

Traditional detection and prewarning methods based on a single atmospheric electric field (AEF) data source often fail to accurately assess thunderstorm weather conditions. This article proposes a thunderstorm detection method that employs weighted multisource fusion of precipitation and AEF features, achieving a high-precision evaluation through multimodal feature fusion and machine learning model optimization. Based on the detection range of AEF apparatus, spatially matched radar chart precipitation data are extracted to construct physically meaningful precipitation statistic features and time-varying AEF features. Using feature importance analysis, the physical contribution of each feature is determined, and these features are weighted and fused accordingly to generate enhanced features. Furthermore, we establish an improved gradient boosting decision tree model that enhances classification performance in the weighted feature space by adaptively adjusting the learning rate. Experimental results demonstrate that this method achieves a highly competitive F1-score of 0.92 in thunderstorm recognition tasks, representing a 35.3% improvement over the traditional AEF threshold method. The proposed weighted fusion framework provides a novel solution for thunderstorm detection and prewarning through the synergistic use of ground-based AEF and radar-based precipitation data.

Stress conditions inhibit mRNA export, but mRNAs encoding heat shock proteins continue to be efficiently exported from the nucleus during stress. How HSP mRNAs bypass this stress-associated export inhibition was not known. Here, we show that HSF1, the transcription factor that binds HSP promoters after stress to induce their transcription, interacts with the nuclear pore-associating TPR protein in a stress-responsive manner. TPR is brought into proximity of the HSP70 promoter after stress and preferentially associates with mRNAs transcribed from this promoter. Disruption of the HSF1-TPR interaction inhibits the export of mRNAs expressed from the HSP70 promoter, both endogenous HSP70 mRNA and a luciferase reporter mRNA. These results suggest that HSP mRNA export escapes stress inhibition via HSF1-mediated recruitment of the nuclear pore-associating protein TPR to HSP genes, thereby functionally connecting the first and last nuclear steps of the gene expression pathway, transcription and mRNA export.

Extracting the effective features for texture description and classification has always been the hot spot of the texture analysis. In this paper, according to different texture of traditional Chinese painting, we use a kind of Gabor filter technique to classify the painting. By texture feature extraction, first of all, we preprocess the traditional Chinese painting images with geometric normalization and light normalization, after that we process the group of the Gabor filter of high dimensional feature vectors by principal component analysis (PCA) for dimension reduction. Finally, support vector machine (SVM) method is employed for texture classification. The accuracy rate of this classification method can reach 95.5%.