Re-Expression of AKAP12 Inhibits Progression and Metastasis Potential of Colorectal Carcinoma In Vivo and In Vitro

Background: AKAP12/Gravin (A kinase anchor protein 12) is one of the A-kinase scaffold proteins and a potential tumor suppressor gene in human primary cancers. Our recent study demonstrated the highly recurrent loss of AKAP12 in colorectal cancer and AKAP12 reexpression inhibited proliferation and anchorage-independent growth in colorectal cancer cells, implicating AKAP12 in colorectal cancer pathogenesis. Methods: To evaluate the effect of this gene on the progression and metastasis of colorectal cancer, we examined the impact of overexpressing AKAP12 in the AKAP12-negative human colorectal cancer cell line LoVo, the single clone (LoVo-AKAP12) compared to mock-transfected cells (LoVo-CON). Results: pCMV6-AKAP12-mediated AKAP12 re-expression induced apoptosis (3 % to 12.7%, p,0.01), migration (89.667.5 cells to 31.064.1 cells, p,0.01) and invasion (82.765.2 cells to 24.763.3 cells, p,0.01) of LoVo cells in vitro compared to control cells. Nude mice injected with LoVo-AKAP12 cells had both significantly reduced tumor volume (p,0.01) and increased apoptosis compared to mice given AKAP12-CON. The quantitative human-specific Alu PCR analysis showed overexpression of AKAP12 suppressed the number of intravasated cells in vivo (p,0.01). Conclusion: These results demonstrate that AKAP12 may play an important role in tumor growth suppression and the survival of human colorectal cancer

A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

As more and more Arabic texts emerged on the Internet, extracting important information from these Arabic texts is especially useful. As a fundamental technology, Named entity recognition (NER) serves as the core component in information extraction technology, while also playing a critical role in many other Natural Language Processing (NLP) systems, such as question answering and knowledge graph building. In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model. Specifically, we first introduce the background of Arabic NER, including the characteristics of Arabic and existing resources for Arabic NER. Then, we systematically review the development of Arabic NER methods. Traditional Arabic NER systems focus on feature engineering and designing domain-specific rules. In recent years, deep learning methods achieve significant progress by representing texts via continuous vector representations. With the growth of pre-trained language model, Arabic NER yields better performance. Finally, we conclude the method gap between Arabic NER and NER methods from other languages, which helps outline future directions for Arabic NER.Comment: Accepted by IEEE TKD

Application of Artificial Intelligence Technologies in a Cloud-based Platform for ECG Analysis to Support the Diagnosis of a Critical Electrocardiography in Primary Care

BackgroundThe cloud-based platform for electrocardiography (ECG) analysis plays a supporting role in the prevention and treatment of cardiovascular diseases. During the construction of a cloud-based platform for ECG analysis, problems that should be focused and addressed are exploring ways to better use artificial intelligence (AI) technologies supporting ECG analysis, and improving the process and effectiveness of AI-aided diagnosis of a critical ECG.ObjectiveTo explore the use of AI technologies in a cloud-based platform for ECG analysis to support the diagnosis of a critical ECG in primary care.MethodsThe 12-lead resting ECGs (n=20 808) uploaded to Nalong Cloud-based ECG Analysis Platform by primary healthcare institutions were selected from June 2019 to June 2021. After being interpreted by AI-based algorithms and physicians, respectively, ECG findings were classified into critical group (critical ECGs) , normal group (normal ECGs) , and positive group (abnormal but not critical ECGs) . The results interpreted by the AI-based algorithm were compared with those interpreted by physicians (defined as the gold standard) to assess the diagnostic agreement and coincidence rate between AI-based and physician-based interpretations, and to assess the diagnostic sensitivity, and positive predictive value of AI-based interpretation. And the mean time for making diagnoses of three groups of ECGs was calculated.ResultsBy the AI-based interpretation, 619, 15 634 and 45 55 ECGs were included in the critical, positive, and normal groups, respectively. And by the physician-based interpretation, 619, 15 759 and 4 430 ECGs were included in the critical, positive, and normal groups, respectively. There was high agreement between AI-based and physician-based interpretation results of ECGs〔Kappa=0.984, 95%CI (0.982, 0.987) , P<0.001〕, with a diagnostic coincidence rate of 99.4%. The diagnostic sensitivity and positive predictive value of AI-based interpretation for ECGs was 99.4%, and 100.0%, respectively. The mean time for making diagnoses of critical ECGs, abnormal but not critical ECGs, and normal ECGs was statistically different (P<0.001) , the mean time of critical critical ECGs was shorter than normal ECGs and abnormal but not critical ECGs (P<0.001) .ConclusionAI technologies used in a cloud-based platform for ECG analysis could provide physicians with support for interpreting ECGs, which may contribute to improving the interpretation accuracy, optimizing the diagnostic process, shortening the time for diagnosing a critical ECG, and the treating of critical patients in primary care

Direction-Preserving and Schur-Monotonic Semi-Separable Approximations of Symmetric Positive Definite Matrices}

For a given symmetric positive definite matrix A {element_of} R{sup N x N}, we develop a fast and backward stable algorithm to approximate A by a symmetric positive-definite semi-separable matrix, accurate to a constant multiple of any prescribed tolerance. In addition, this algorithm preserves the product, AZ, for a given matrix Z {element_of} R{sup N x d}, where d << N. Our algorithm guarantees the positive-definiteness of the semi-separable matrix by embedding an approximation strategy inside a Cholesky factorization procedure to ensure that the Schur complements during the Cholesky factorization all remain positive definite after approximation. It uses a robust direction-preserving approximation scheme to ensure the preservation of AZ. We present numerical experiments and discuss potential implications of our work

Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking

Entity linking (EL) for the rapidly growing short text (e.g. search queries and news titles) is critical to industrial applications. Most existing approaches relying on adequate context for long text EL are not effective for the concise and sparse short text. In this paper, we propose a novel framework called Multi-turn Multiple-choice Machine reading comprehension (M3}) to solve the short text EL from a new perspective: a query is generated for each ambiguous mention exploiting its surrounding context, and an option selection module is employed to identify the golden entity from candidates using the query. In this way, M3 framework sufficiently interacts limited context with candidate entities during the encoding process, as well as implicitly considers the dissimilarities inside the candidate bunch in the selection stage. In addition, we design a two-stage verifier incorporated into M3 to address the commonly existed unlinkable problem in short text. To further consider the topical coherence and interdependence among referred entities, M3 leverages a multi-turn fashion to deal with mentions in a sequence manner by retrospecting historical cues. Evaluation shows that our M3 framework achieves the state-of-the-art performance on five Chinese and English datasets for the real-world short text EL.Comment: Accepted at AAAI 202

AKAKP12 suppresses <i>in vitro</i> migration and invasion ability of LoVo cells.

<p>(A) LoVo-AKAP12 and LoVo-CON cells penetrated through the transwell chambers and were photographed at ×100 magnification. (B) The <i>in vitro</i> migration ability of LoVo-AKAP12 cells and LoVo-CON cells was measured by determining the number of cells that migrated through the transwell chamber. Columns, mean values; bar, SD. * <i>p</i><0.01. (C) LoVo-AKAP12 and LoVo-CON cells that migrated through the transwell chambers and photographed at ×100 magnification. (D) The <i>in vitro</i> migration ability of LoVo-AKAP12 cells and LoVo-CON cells was measured by determining the number of matrigel-coated cells that penetrated through the transwell chambers. Columns, mean values; bar, SD. *<i>p</i><0.01.</p