HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.

BACKGROUND: Advances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis. RESULTS: Extensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity. CONCLUSIONS: HyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.

The Altered Functions of Shelterin Components in ALT Cells.

Telomeres are nucleoprotein complexes that cap the ends of eukaryotic linear chromosomes. Telomeric DNA is bound by shelterin protein complex to prevent telomeric chromosome ends from being recognized as damaged sites for abnormal repair. To overcome the end replication problem, cancer cells mostly preserve their telomeres by reactivating telomerase, but a minority (10-15%) of cancer cells use a homologous recombination-based pathway called alternative lengthening of telomeres (ALT). Recent studies have found that shelterin components play an important role in the ALT mechanism. The binding of TRF1, TRF2, and RAP1 to telomeres attenuates ALT activation, while the maintenance of ALT telomere requires TRF1 and TRF2. POT1 and TPP1 can also influence the occurrence of ALT. The elucidation of how shelterin regulates the initiation of ALT remains elusive. This review presents a comprehensive overview of the current findings on the regulation of ALT by shelterin components, aiming to enhance the insight into the altered functions of shelterin components in ALT cells and to identify potential targets for the treatment of ALT tumor cells.

Modified iPOND revealed the role of mutant p53 in promoting helicase function and telomere maintenance.

The G-rich DNA, such as telomere, tends to form G-quadruplex (G4) structure, which slows down the replication fork progression, induces replication stress, and becomes the chromosome fragile sites. Here we described a molecular strategy that cells developed to overcome the DNA replication stress via DNA helicase regulation. The p53N236S (p53S) mutation has been found in the Werner syndrome mouse embryo fibroblast (MEFs) escaped from senescence, could be the driving force for cell escaping senescence. We revealed that the p53S could transcriptionally up-regulate DNA helicases expression, including Wrn, Blm, Timeless, Ddx, Mcm, Gins, Fanc, as well as telomere specific proteins Terf1, Pot1, through which p53S promoted the unwinding of G4 structures, and protected the cells from DNA replication stress induced by G4 stabilizer. By modified iPOND (isolation of proteins on nascent DNA) assay and telomere assay, we demonstrated that the p53S could promote the recruitment of those helicases to the DNA replication forks, facilitated the maintenance of telomere, and prevent the telomere dysfunction induced by G4 stabilizer. Interestingly, we did not observe the function of promoting G4 resolving and facilitating telomere lengthening in the cells with Li-Fraumeni Syndrome mutation-p53R172H (p53H), which suggests that this is the specific gain of function for p53S. Together our data suggest that the p53S could gain the new function of releasing the replication stress via regulating the helicase function and G4 structure, which benefits telomere lengthening. This strategy could be applied to the treatment of diseases caused by telomere replication stress.

Rethinking Prior-Guided Face Super-Resolution: A New Paradigm With Facial Component Prior.

Recently, facial priors (e.g., facial parsing maps and facial landmarks) have been widely employed in prior-guided face super-resolution (FSR) because it provides the location of facial components and facial structure information, and helps predict the missing high-frequency (HF) information. However, most existing approaches suffer from two shortcomings: 1) the extracted facial priors are inaccurate since they are extracted from low-resolution (LR) or low-quality super-resolved (SR) face images and 2) they only consider embedding facial priors into the reconstruction process from LR to SR face images, thus failing to explore facial priors to generate LR face image. In this article, we propose a novel pre-prior guided approach that extracts facial prior information from original high-resolution (HR) face images and embeds them into LR ones to obtain HF information-rich LR face images, thereby improving the performance of face reconstruction. Specifically, a novel component hybrid method is proposed, which fuses HR facial components and LR facial background to generate new LR face images (namely, LRmix) via facial parsing maps extracted from HR face images. Furthermore, we design a component hybrid network (CHNet) that learns the LR to LRmix mapping function to ensure that the LRmix can be obtained from LR face images in testing and real-world datasets. Experimental results show that our proposed scheme significantly improves the reconstruction performance for FSR.

Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model.

The current power distribution Internet of Things (PDIoT) lacks security protection terminals and techniques. Network security has a large exposure surface that can be attacked from multiple paths. In addition, there are many network security vulnerabilities and weak security protection capabilities of power distribution Internet of Things terminals. Therefore, it is crucial to conduct a scientific assessment of the security of PDIoT. However, traditional security assessment methods are relatively subjective and ambiguous. To address the problems, we propose to use the entropy-weight method and cloud model theory to assess the security risk of the PDIoT. We first analyze the factors of security risks in PDIoT systems and establish a three-layer PDIoT security evaluation index system, including a perception layer, network layer, and application layer. The index system has three first-level indicators and sixteen second-level indicators. Then, the entropy-weight method is used to optimize the weight of each index. Additionally, the cloud model theory is employed to calculate the affiliation degree and eigenvalue of each evaluation index. Based on a comprehensive analysis of all evaluation indexes, we can achieve the security level of PDIoT. Taking the PDIoT of Meizhou Power Supply Bureau of Guangdong Power Grid as an example for empirical testing, the experimental results show that the evaluation results are consistent with the actual situation, which proves that the proposed method is effective and feasible.

p53N236S Activates Autophagy in Response to Hypoxic Stress Induced by DFO.

Hypoxia can lead to stabilization of the tumor suppressor gene p53 and cell death. However, p53 mutations could promote cell survival in a hypoxic environment. In this study, we found that p53N236S (p53N239S in humans, hereinafter referred to as p53S) mutant mouse embryonic fibroblasts (MEFs) resistant to deferoxamine (DFO) mimic a hypoxic environment. Further, Western blot and flow cytometry showed reduced apoptosis in p53S/S cells compared to WT after DFO treatment, suggesting an antiapoptosis function of p53S mutation in response to hypoxia-mimetic DFO. Instead, p53S/S cells underwent autophagy in response to hypoxia stress presumably through inhibition of the AKT/mTOR pathway, and this process was coupled with nuclear translocation of p53S protein. To understand the relationship between autophagy and apoptosis in p53S/S cells in response to hypoxia, the autophagic inhibitor 3-MA was used to treat both WT and p53S/S cells after DFO exposure. Both apoptotic signaling and cell death were enhanced by autophagy inhibition in p53S/S cells. In addition, the mitochondrial membrane potential (MMP) and the ROS level results indicated that p53S might initiate mitophagy to clear up damaged mitochondria in response to hypoxic stress, thus increasing the proportion of intact mitochondria and maintaining cell survival. In conclusion, the p53S mutant activates autophagy instead of inducing an apoptotic process in response to hypoxia stress to protect cells from death.

Alternative Lengthening of Telomeres and Mediated Telomere Synthesis.

Telomeres are DNA-protein complexes that protect eukaryotic chromosome ends from being erroneously repaired by the DNA damage repair system, and the length of telomeres indicates the replicative potential of the cell. Telomeres shorten during each division of the cell, resulting in telomeric damage and replicative senescence. Tumor cells tend to ensure cell proliferation potential and genomic stability by activating telomere maintenance mechanisms (TMMs) for telomere lengthening. The alternative lengthening of telomeres (ALT) pathway is the most frequently activated TMM in tumors of mesenchymal and neuroepithelial origin, and ALT also frequently occurs during experimental cellular immortalization of mesenchymal cells. ALT is a process that relies on homologous recombination (HR) to elongate telomeres. However, some processes in the ALT mechanism remain poorly understood. Here, we review the most recent understanding of ALT mechanisms and processes, which may help us to better understand how the ALT pathway is activated in cancer cells and determine the potential therapeutic targets in ALT pathway-stabilized tumors.

Constant Tension Control System of High-Voltage Coil Winding Machine Based on Smith Predictor-Optimized Active Disturbance Rejection Control Algorithm.

In the production process of high-voltage coils, a constant tension control system is designed to improve the quality of the transformer. The system is composed of a controller, execution structure, detection structure, etc. The active disturbance rejection control (ADRC), optimized by the Smith predictor (SP), is adopted to achieve constant tension control. The experiment results show that the tension control system based on the SP-ADRC has higher control accuracy, shorter stabilization time and stronger anti-interference ability compared with the traditional PID algorithm. The actual experiment shows that the constant tension control system of the high-voltage coil winding machine based on SP-ADRC has a superior control effect and high practical value.

Resolution Improvement of Light Field Imaging via a Nematic Liquid Crystal Microlens with Added Multi-Walled Carbon Nanotubes.

A relatively simple method to improve the image resolution of light field based on a liquid crystal (LC) microlens doped with multi-walled carbon nanotubes (MWCNTs) was developed and evaluated. As the nanoparticles were doped in LC, its electro-optical features could enhance, leading to a short response time compared to the pure LC microlens. With the maximum use of the proposed LC microlens, a method combining aperiodicity extraction and weighted average algorithm was adopted to realize the high-resolution light field imaging. The aperiodicity extraction method was proposed, which could effectively improve resolution of view angle image. For synthesizing the full resolution image at 0 Vrms and the extracted view angle image of light field imaging at 2.0 Vrms, the final high-resolution light field imaging could be obtained in a short time by weighted average algorithm. In this way, the common problem of low resolution in light field imaging could be solved. This proposed method was in good agreement with our experimental results. And it was also in line with the development of the trend of the smart imaging sensor combining algorithm with hardware.

Cross-Weather Image Alignment via Latent Generative Model with Intensity Consistency.

Image alignment/registration/correspondence is a critical prerequisite for many vision-based tasks, and it has been widely studied in computer vision. However, aligning images from different domains, such as cross-weather/season road scenes, remains a challenging problem. Inspired by the success of classic intensity-constancy-based image alignment methods and the modern generative adversarial network (GAN) technology, we propose a cross-weather road scene alignment method called latent generative model with intensity constancy. From a novel perspective, the alignment problem is formulated as a constrained 2D flow optimization problem with latent encoding, which can be decoded into an intensity-constancy image on the latent image manifold. The manifold is parameterized by a pre-trained GAN, which is able to capture statistic characteristics from large datasets. Moreover, we employ the learned manifold to constrain the warped latent image identical to the target image, thereby producing a realistic warping effect. Experimental results on several cross-weather/season road scene datasets demonstrate that our approach can significantly outperform the state-of-the-art methods.

Focal stack camera in all-in-focus imaging via an electrically tunable liquid crystal lens doped with multi-walled carbon nanotubes.

A focal stack camera, based on an electrically tunable-focusing liquid crystal (LC) lens doped with multi-walled carbon nanotubes, is proposed to generate a single all-in-focus image of a 3D scene without depth map in a relatively short time. Focal sweep strategy of the camera is devised. Both its depth of field (DOF) and focal sweep speed are analyzed and deduced. Nano doping method is adopted to improve electro-optical features of the LC lens. To efficiently produce all-in-focus image, a weighted average algorithm for all images in the focal stack is utilized. The experiments show that the result is a high contrast at sensor resolution. It is greatly potential in optical compact 3D imaging system.

Improvement in imaging contrast feature of liquid crystal lens with the dopant of multi-walled carbon nanotubes.

An effective method to fabricate a high-contrast imaging liquid crystal (LC) lens with a relatively fast response time is proposed. This electrically tunable-focus LC lens is doped with multi-walled carbon nanotubes (MWCNTs) and has a high-resistance layer. A grinding method is utilized to lower agglomeration of doping MWCNTs. With respect to the high-resistance layer, it can make maximum use of doped nanoparticles to behave with high-quality lens character. The principles of improvement in electro-optical properties are discussed. The experimental results show that the proposed LC lens is effectively improved. The LC lens has great potential in compact imaging systems.

MsLRR: a unified multiscale low-rank representation for image segmentation.

In this paper, we present an efficient multiscale low-rank representation for image segmentation. Our method begins with partitioning the input images into a set of superpixels, followed by seeking the optimal superpixel-pair affinity matrix, both of which are performed at multiple scales of the input images. Since low-level superpixel features are usually corrupted by image noise, we propose to infer the low-rank refined affinity matrix. The inference is guided by two observations on natural images. First, looking into a single image, local small-size image patterns tend to recur frequently within the same semantic region, but may not appear in semantically different regions. The internal image statistics are referred to as replication prior, and we quantitatively justified it on real image databases. Second, the affinity matrices at different scales should be consistently solved, which leads to the cross-scale consistency constraint. We formulate these two purposes with one unified formulation and develop an efficient optimization procedure. The proposed representation can be used for both unsupervised or supervised image segmentation tasks. Our experiments on public data sets demonstrate the presented method can substantially improve segmentation accuracy.

Three-dimensional imaging based on electronically adaptive liquid crystal lens.

In this paper, we present a relatively simple method to acquire a 3D image based on an electrically controlled liquid crystal (LC) lens. Its advantage is that this proposed method does not need any mechanical movements to acquire a 3D image. The tunable-focus LC lens combined with a high-resistance layer (PEDOT) is applied by an overdrive method to become a key optical component for use in a 3D imaging system. Multiple 2D images of slightly different perspectives are recorded, respectively, and 3D images, according to a proposed mapping and projection method, can be reconstructed. This is the first report, to the best of our knowledge, on using an LC lens to reconstruct 3D images. The proposed 3D imaging system is novel for its compact and smart features, so it is attractive for some compact 3D imaging systems.