Ordered and deterministic cancer genome evolution after p53 loss

Although p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks

Asymmetrical Three-Dimensional Conformal Imaging Lens

Absolute instrument refers to a media that can make light rays to propagate in a closed orbit and perform imaging and self-imaging. In the past few decades, traditional investigations into absolute instrument have been centered on the two-dimensional plane and rotational symmetry situations, and have paid less attention to three-dimensional counterparts. In this article, we design two types of three-dimensional non-spherically symmetric absolute instruments based on conformal inverse transformation, which originated from the three-dimensional Luneburg lens and Lissajous lens. We carry out ray tracing on the optical performance of these new lenses and analyze the imaging laws. Our work enlarges the family of absolute instruments from two dimensions to three dimensions and symmetry to asymmetry, which may allow for imaging applications in optical waves

Graph Construction for Salient Object Detection in Videos

Recently many graph-based salient region/object detection methods have been developed. They are rather effective for still images. However, little attention has been paid to salient region detection in videos. This paper addresses salient region detection in videos. A unified approach towards graph construction for salient object detection in videos is proposed. The proposed method combines static appearance and motion cues to construct graph, enabling a direct extension of original graph based salient region detection to video processing. To maintain coherence in both intra- and inter-frames, a spatial-temporal smoothing operation is proposed on a structured graph derived from consecutive frames. The effectiveness of the proposed method is tested and validated using seven videos from two video datasets

Molecular Dynamics Simulation of Methane Adsorption and Diffusion: A Case Study of Low-Rank Coal in Fukang Area, Southern Junggar Basin

Adsorption and diffusion are the key factors affecting coalbed methane (CBM) accumulation, resource assessment and production prediction. To study the adsorption and diffusion mechanism of Fukang low-rank coal at the microscopic level, samples of Fukang low-rank coal were collected, and the elemental composition, carbon type distribution and functional group type of the Fukang low-rank coal structure were determined by elemental analysis (Ea), Fourier-transform interferometric radiometer (FTIR), X-ray photoelectron spectroscopy (XPS) and 13C nuclear magnetic resonance (13C NMR) experiments to construct a 2D molecular structure of the coal and a 3D macromolecular structure model. The adsorption and diffusion characteristics of methane were researched by giant regular Monte Carlo (GCMC) and molecular dynamics (MD) simulation methods. The results showed that the excess adsorption amount of methane increased and then decreased with the increase in pressure. The diffusion of methane showed two stages with increasing pressure: a sharp decrease in the diffusion coefficient from 0.5 to 5.0 MPa and a slow decrease in the diffusion coefficient from 5.0 to 15.0 MPa. The lower the pressure, the larger the effective radius of the CH4 and C atoms, and the higher the temperature, the more pronounced the diffusion and the larger the effective radius

Molecular Dynamics Simulation of Methane Adsorption and Diffusion: A Case Study of Low-Rank Coal in Fukang Area, Southern Junggar Basin

Adsorption and diffusion are the key factors affecting coalbed methane (CBM) accumulation, resource assessment and production prediction. To study the adsorption and diffusion mechanism of Fukang low-rank coal at the microscopic level, samples of Fukang low-rank coal were collected, and the elemental composition, carbon type distribution and functional group type of the Fukang low-rank coal structure were determined by elemental analysis (Ea), Fourier-transform interferometric radiometer (FTIR), X-ray photoelectron spectroscopy (XPS) and 13C nuclear magnetic resonance (13C NMR) experiments to construct a 2D molecular structure of the coal and a 3D macromolecular structure model. The adsorption and diffusion characteristics of methane were researched by giant regular Monte Carlo (GCMC) and molecular dynamics (MD) simulation methods. The results showed that the excess adsorption amount of methane increased and then decreased with the increase in pressure. The diffusion of methane showed two stages with increasing pressure: a sharp decrease in the diffusion coefficient from 0.5 to 5.0 MPa and a slow decrease in the diffusion coefficient from 5.0 to 15.0 MPa. The lower the pressure, the larger the effective radius of the CH4 and C atoms, and the higher the temperature, the more pronounced the diffusion and the larger the effective radius

Adaptive Multi-Level Region Merging for Salient Object Detection

Most existing salient object detection algorithms face the problem of either under or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph based merging scheme is developed to reassemble regions based on their shared contour strength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance

Adaptive Multi-Level Region Merging for Salient Object Detection

Most existing salient object detection algorithms face the problem of either under or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph based merging scheme is developed to reassemble regions based on their shared contourstrength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance

Stratification of TAD boundaries reveals preferential insulation of super-enhancers by strong boundaries

Topologically associating domains (TADs) detected by Hi-C technologies are megabase-scale areas of highly interacting chromatin. Here Gong, Lazaris et al. develop a computational approach to improve the reproducibility of Hi-C contact matrices and stratify TAD boundaries based on their insulating strength

Nanostructured 08Li2FeSiO4/04Li2SiO 3/C composite cathode material with enhanced electrochemical performance for lithium-ion batteries

A strategy is proposed and developed to promote Li+ diffusion in polyanion cathode materials such as 0.8Li2FeSiO4/0. 4Li2SiO3/C with the incorporation of Li 2SiO3 as a lithium ionic conductive matrix. It is shown that the presence of Li2SiO3 separates the Li 2FeSiO4 particles into small domains of a few nanometres and provides a fast Li+ diffusion channel, thus effectively enhancing Li+ diffusion in the 0.8Li2FeSiO4/0.4Li 2SiO3/C composite. As a result, the composite material shows enhanced electrochemical performance and delivers a capacity as high as 240 mA h g-1 (corresponding to 1.44 electrons exchange per active Li2FeSiO4 formula unit) with good cyclic stability at 30 掳C. The XRD and FTIR results indicate that the Li2SiO3 component exists in an amorphous phase. SEM and TEM analyses show an aggregate structure consisting of primary nanocrystallites (about tens of nanometres in diameter). The primary particles consist of a crystal Li2FeSiO 4 phase and an amorphous Li2SiO3 and C, and a nanocrystalline Li2FeSiO4 surrounded by amorphous Li 2SiO3 and C which are well known as a lithium ion conductor and electron conductor. The smaller nanoparticles of Li 2FeSiO4 and the presence of lithium ionic and electronic conducting amorphous Li2SiO3 and carbon matrix both contributed to the enhanced electrochemical performance of the composite. 漏 2012 The Royal Society of Chemistry