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With the development of $3D$ visualization technology, the amount of geological data information is increasing, and the interactive display of big data faces severe challenges. Because traditional volume rendering methods cannot entirely load large-scale data into the memory owing to hardware limitations, a visualization method based on variational deep embedding clustering fusion Hilbert R-tree is proposed to solve slow display and stuttering issues when rendering massive geological data. By constructing an efficient data index structure, deep clustering algorithms and space-filling curves can be integrated into the data structure to improve the indexing efficiency. In addition, this method combines time forecasting, data scheduling, and loading modules to improve the accuracy and real-time data display rate, thereby improving the stability of $3D$ visualization of large-scale geological data. This method uses real geological data as the experimental dataset, comparing and analyzing the existing index structure and time-series prediction method. The experimental results indicate that when comparing the index of the variational deep embedded clustering-Hilbert R-tree ( $VDEC-HRT$ ) with that of the K-means Hilbert R-tree ( $KHRT$ ), the time required is reduced by 55.67%, the viewpoint prediction correctness of the proposed method is improved by 22.7% compared with Lagrange interpolation algorithm. And the overall rendering performance and quality of the system achieve the expected results. Ours experiments prove the feasibility and effectiveness of the proposed scheme in the visualization of large-scale geological data.

Innovation in facial recognition technology is urgent in the current epidemic situation. In order to extract more face features to perform facial recognition on images of faces with masks, we propose a multi-step iterative face-mask segmentation algorithm (MISA). The improved Mask R-CNN algorithm is used to detect the target in the face region to realize coarse segmentation, and the generalization ability and accuracy of the segmentation are improved. Then, the proposed approach uses an R-Pairwise Differential Siamese Network (R-PDSN) to train a mask occlusion classifier to subdivide the edge blocks of coarse segmentation results. The segmentation accuracy is further improved by optimizing the edge information of the masks step by step. The self-built dataset of faces with masks was used for training and testing. The experimental results showed that the mean pixel accuracy of the proposed method was improved by 2.69% compared with the original Mask R-CNN segmentation algorithm, and the target detection accuracy was more than 98%. These results indicate that the proposed method can achieve good segmentation performance on face images with complex backgrounds, self-occlusion and different types of masks. These results demonstrate that our method can improve the accuracy of segmentation methods for imaged of faces wearing masks.

With the development of microelectronic technology, the reliability of devices in a complex electromagnetic environment has become one of the greatest challenges in the semiconductor industry. On this basis, a phenomenon of nonlinear transient response is observed in high-power microwave (HPM)-radiating AlGaAs/InGaAs pseudomorphic high-electron-mobility transistors (pHEMTs). This abnormal response is induced before the thermal failure, causing disturbances to the circuit. To understand this phenomenon, a detailed mechanism analysis is proposed. The analysis shows that the nonlinear response is initially associated with the 2DEG velocity saturation, then a breakdown process is induced by the tunneling and impact ionization combined effect. Within each radiation period, the channel current changes its direction twice under the influence of the HPM field. The nonlinear response current Id is derived from the theoretical analysis. TCAD simulations demonstrate the saturation and breakdown process. Corresponding experiments are performed using a Ka-band low-noise amplifier (LNA) chip. The results support the theory well.

Although cancer has long been a major public health problem worldwide, conquering cancer still remains unachievable due to its complexity and diversity. With the development of high-throughput sequencing technologies, the combination of conventional clinical symptoms and new genetic events is becoming effective and has been approved for precise prediction and innovative diagnostic strategies. Epigenetic modification (e.g., DNA methylation) is an important mechanism of transcriptional control in normal or disease functions. Depending on the unique methylation profiles, an important possibility raised is that the characteristic of dys-methylation could provide a new molecular marker system for the identification of the major forms of tumors. In this study, we attempted to distinguish different tumor types. On the basis of DNA methylation data from PanCanAtlas in The Cancer Genome Atlas, we applied mRMR and MCFS methods together to identify the decision rules for distinguishing 33 different tumor types and ranked the features that characterized methylation level. This study highlights the considerable application potential of methylation features in cancer diagnosis and provides insight into novel therapeutic targets.

Metastasis is the leading cause of cancer-related death. A small proportion of tumor cells can spread to other tissues through the lymph system or bloodstream and colonize in a new microenvironment. However, not all tumors from the primary site can metastasize. What is the difference between metastatic and primary tumors? Can such difference be preserved in widely used patient-derived xenografts (PDX)? To answer these questions, we analyzed the single-cell gene expression profiles of 36 cells from PDX of metastatic renal cell carcinoma (mRCC), 47 cells from PDX of primary RCC (pRCC), and 35 cells from parental mRCC (pt-mRCC). First, the gene expression patterns of PDX-mRCC and PDX-pRCC were compared, and the PDX-mRCC signatures were generated. Such signatures reflected the difference between metastatic and primary tumors. Second, the pt-mRCC were tested on whether they can be correctly classified into the PDX-mRCC class rather than PDX-pRCC. We found that pt-mRCC were very similar with PDX-mRCC. Our results prove that the PDX is a great research model for metastatic tumors since it preserved the essences for tumor metastasis. Our results justify the applications of PDX in metastatic tumor studies.

An embryo develops from a single-celled zygote, which produces a multi-cellular organism by mitosis. Due to the complication of processes and mechanisms, research on embryo cell clusters in different early embryo developmental stages with significant phenotypic differences is still lacking. In this work, we identified some gene characters and expression rules to classify these individual cells using several advanced computational methods. The single cell expression profiles of embryo cells were analyzed by the Monte Carlo feature selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) method, incorporating support vector machine (SVM), applied on such list to extract key gene characters. These gene characters include KHDC1, HMGN1, DCP, GDF9, RNF11, DNMT3L, and CDX1. Furthermore, a rule learning algorithm, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), was applied to the informative features yielded by MCFS method, producing a group of classification rules. These rules can clearly uncover different expression patterns on cells in different stages. This study provided a group of effective gene signatures and rules for embryo cell subtyping and presented an applicable computational tool to further dig into the regulatory mechanisms of embryo development.

The CRISPR/Cas9 system is a creative and innovative gene editing biotechnology tool in genetic engineering. Although several achievements have been attained using the CRISPR/Cas9 system, it is still a challenge to avoid off-target effects and improve the editing efficacy. Previous efforts on evaluating the efficacy and designing the guide RNA mainly focused on DNA properties. However, some DNA features have not been characterized but can be reflected by protein properties, such as the disorder features and the sequence conservation. In this paper, we provided a computational framework to identify important features related to the efficacy of CRISPR/Cas9 focusing on the properties of the proteins encoded by the target DNA fragments. The feature selection method, maximal-relevance-minimal-redundancy, was adopted to analyze these features. And incremental feature selection together with support vector machine, were employed to extract optimal features, on which an optimal classifier can be constructed. As a result, 152 important features were extracted, with which an optimal classifier based on support vector machine was built. This classifier obtained the highest MCC value of 0.355. Finally, a series of detailed biological analyses were performed on the optimal features. From the results, we found that some key factors may differentially affect the binding activity of sgRNAs to their targets. Among them, the disorder status of the target protein sequences was found to be a major factor that is related to the efficacy of sgRNAs, suggesting the DNA features associated with the protein disorder status could also affect the CRISPR/Cas9 efficacy.