1,480 research outputs found

    The Influence of Chinese Culture–Poetry to Southeast Asian Ethnic Chinese Writers

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    The poet James (Teng Choon) Na characterized the unique feel of Filipino Chinese literature as traceable to its Southeast Asian roots. He discussed the hibernation of Philippine-Chinese literature, Philippine-Chinese literature under the guidance of the mass media, and new avenues for the development of Philippine-Chinese literature. He concluded with an optimistic note that Philippine-Chinese literature will flourish despite the challenges of the times

    A Convenient Adomian-Pade Technique for the Nonlinear Oscillator Equation

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    Very recently, the convenient way to calculate the Adomian series was suggested. This paper combines this technique and the Pade approximation to develop some new iteration schemes. Then, the combined method is applied to nonlinear models and the residual functions illustrate the accuracies and conveniences

    Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

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    Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis

    Muon and Pion Identification at BESIII Based on Machine Learning Algorithm

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    BESIII is designed to study physics in the τ-charm energy region utilizing the high luminosity BEPCII. For collision physics experiments like the BESIII experiment, particle identification (PID) is one of the most important and commonly used tools for physics analysis. The effective µ/π identification performance is of great significance for most of BESIII physics analysis. However, due to the close masses of these two particles, as well as the intrinsic correlation between multiple detector information, traditional methods at BESIII is facing challenges in µ/π identification. In recent decades, machine learning (ML) techniques have been rapidly developed and have shown successful applications in HEP experiments. The PID based on ML provides powerful capability of combining more detection information from all sub-detectors with the data-driven approach. In this article, targeting at the µ/π identification problem at the BESIII experiment, we have developed a new PID algorithm based on the gradient boosted decision tree (BDT) model. Preliminary results show that the XGBoost classifier provides obviously higher discrimination power than traditional methods. In addition, based on the substantial amount of high-quality data taken by the BESIII detector, a method of evaluating and suppressing the systematical error of the ML model is also introduced, which is critical for applying the model to physics studies

    Marek's disease virus-encoded miR-155 ortholog critical for the induction of lymphomas is not essential for the proliferation of transformed cell lines

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    MicroRNAs (miRNAs) are small noncoding RNAs with profound regulatory roles in many areas of biology, including cancer. MicroRNA 155 (miR-155), one of the extensively studied multifunctional miRNAs, is important in several human malignancies such as diffuse large B cell lymphoma and chronic lymphocytic leukemia. Moreover, miR-155 orthologs KSHV-miR-K12-11 and MDV-miR-M4, encoded by Kaposi's sarcoma-associated herpesvirus (KSHV) and Marek's disease virus (MDV), respectively, are also involved in oncogenesis. In MDV-induced T-cell lymphomas and in lymphoblastoid cell lines derived from them, MDV-miR-M4 is highly expressed. Using excellent disease models of infection in natural avian hosts, we showed previously that MDV-miR-M4 is critical for the induction of T-cell lymphomas as mutant viruses with precise deletions were significantly compromised in their oncogenicity. However, those studies did not elucidate whether continued expression of MDV-miR-M4 is essential for maintaining the transformed phenotype of tumor cells. Here using an in situ CRISPR/Cas9 editing approach, we deleted MDV-miR-M4 from the MDV-induced lymphoma-derived lymphoblastoid cell line MDCC-HP8. Precise deletion of MDV-miR-M4 was confirmed by PCR, sequencing, quantitative reverse transcription-PCR (qRT-PCR), and functional analysis. Continued proliferation of the MDV-miR-M4-deleted cell lines demonstrated that MDV-miR-M4 expression is not essential for maintaining the transformed phenotype, despite its initial critical role in the induction of lymphomas. Ability to examine the direct role of oncogenic miRNAs in situ in tumor cell lines is valuable in delineating distinct determinants and pathways associated with the induction or maintenance of transformation in cancer cells and will also contribute significantly to gaining further insights into the biology of oncogenic herpesviruses.IMPORTANCE Marek's disease virus (MDV) is an alphaherpesvirus associated with Marek's disease (MD), a highly contagious neoplastic disease of chickens. MD serves as an excellent model for studying virus-induced T-cell lymphomas in the natural chicken hosts. Among the limited set of genes associated with MD oncogenicity, MDV-miR-M4, a highly expressed viral ortholog of the oncogenic miR-155, has received extensive attention due to its direct role in the induction of lymphomas. Using a targeted CRISPR-Cas9-based gene editing approach in MDV-transformed lymphoblastoid cell lines, we show that MDV-miR-M4, despite its critical role in the induction of tumors, is not essential for maintaining the transformed phenotype and continuous proliferation. As far as we know, this was the first study in which precise editing of an oncogenic miRNA was carried out in situ in MD lymphoma-derived cell lines to demonstrate that it is not essential in maintaining the transformed phenotype
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