3,898 research outputs found
Luo Qing's Paintings of Post-Industrial Taiwan and Their Incompatibility with Guohua
This thesis examines the career and artwork of Luo Qing in the context of past artistic movements and current academic discourse. Using Luo Qing and his work as a point of departure, this thesis aims to combine diachronic and synchronic concerns in the arts, specifically art that is made in the medium of ink.
Luo Qing is famous for his inventive style in poetry and ink paintings. The two bodies of work selected,"Here Comes the UFO" and "Asphalt Road", not only exemplify his creative spirit in redefining ink art, they also establish him as a member of the modern Chinese literati, a scholar artist, in Taiwan. Both series were Luo's ongoing projects in the 1980s and the 1990s. A conflict between the traditional and the new was present in Chinese politics and culture at the time, and this tension affected the creative community. The dynamics between Chinese imperial history and modern Chinese industry is the subject of most of Luo's work. He creatively portrayed conflicts between traditional Chinese heritage and contemporary Western commercialism. "Here Comes the UFO"and " Asphalt Road" both depict the modern subject of industrialization in traditional Chinese ink painting format. Luo Qing's novel way of approaching Chinese artistic traditions, both in painting and poetry, validated its importance as a new paradigm. Luo's artistic world depicted in these two bodies of work was representative of a tumultuous era in Chinese history that took place not in China, but in Taiwan.
In stark contrast, the current academic discourse on ink art originated in China and quickly spread through the research of Chinese scholars, most of whom work in North American academia. Compelling debates on ink art's importance and passionate proclamation associating ink art with Chinese nationhood are popular subjects.
These subjects, however, are distant and irrelevant to Luo's early cityscapes. The contemporary paradigm may ignore why Luo Qing came to international fame.
The first part of this thesis profiles Luo's two bodies of work and provides a comprehensive survey of his training and inspiration from the past. The second part connects these works with a thorough overview of scholarship on contemporary ink art. Using Luo's work as an intersecting point of reference, I hope to revive Luo Qing's significance to the Chinese art community and address specific, larger issues concerning contemporary theories on ink art
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GPER-induced signaling is essential for the survival of breast cancer stem cells.
G protein-coupled estrogen receptor-1 (GPER), a member of the G protein-coupled receptor (GPCR) superfamily, mediates estrogen-induced proliferation of normal and malignant breast epithelial cells. However, its role in breast cancer stem cells (BCSCs) remains unclear. Here we showed greater expression of GPER in BCSCs than non-BCSCs of three patient-derived xenografts of ER- /PR+ breast cancers. GPER silencing reduced stemness features of BCSCs as reflected by reduced mammosphere forming capacity in vitro, and tumor growth in vivo with decreased BCSC populations. Comparative phosphoproteomics revealed greater GPER-mediated PKA/BAD signaling in BCSCs. Activation of GPER by its ligands, including tamoxifen (TMX), induced phosphorylation of PKA and BAD-Ser118 to sustain BCSC characteristics. Transfection with a dominant-negative mutant BAD (Ser118Ala) led to reduced cell survival. Taken together, GPER and its downstream signaling play a key role in maintaining the stemness of BCSCs, suggesting that GPER is a potential therapeutic target for eradicating BCSCs
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FAM129B, an antioxidative protein, reduces chemosensitivity by competing with Nrf2 for Keap1 binding.
BackgroundThe transcription factor Nrf2 is a master regulator of antioxidant response. While Nrf2 activation may counter increasing oxidative stress in aging, its activation in cancer can promote cancer progression and metastasis, and confer resistance to chemotherapy and radiotherapy. Thus, Nrf2 has been considered as a key pharmacological target. Unfortunately, there are no specific Nrf2 inhibitors for therapeutic application. Moreover, high Nrf2 activity in many tumors without Keap1 or Nrf2 mutations suggests that alternative mechanisms of Nrf2 regulation exist.MethodsInteraction of FAM129B with Keap1 is demonstrated by immunofluorescence, colocalization, co-immunoprecipitation and mammalian two-hybrid assay. Antioxidative function of FAM129B is analyzed by measuring ROS levels with DCF/flow cytometry, Nrf2 activation using luciferase reporter assay and determination of downstream gene expression by qPCR and wester blotting. Impact of FAM129B on in vivo chemosensitivity is examined in mice bearing breast and colon cancer xenografts. The clinical relevance of FAM129B is assessed by qPCR in breast cancer samples and data mining of publicly available databases.FindingsWe have demonstrated that FAM129B in cancer promotes Nrf2 activity by reducing its ubiquitination through competition with Nrf2 for Keap1 binding via its DLG and ETGE motifs. In addition, FAM129B reduces chemosensitivity by augmenting Nrf2 antioxidative signaling and confers poor prognosis in breast and lung cancer.InterpretationThese findings demonstrate the important role of FAM129B in Nrf2 activation and antioxidative response, and identify FMA129B as a potential therapeutic target. FUND: The Chang Gung Medical Foundation (Taiwan) and the Ministry of Science and Technology (Taiwan)
Learning Deep Latent Spaces for Multi-Label Classification
Multi-label classification is a practical yet challenging task in machine
learning related fields, since it requires the prediction of more than one
label category for each input instance. We propose a novel deep neural networks
(DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this
task. Aiming at better relating feature and label domain data for improved
classification, we uniquely perform joint feature and label embedding by
deriving a deep latent space, followed by the introduction of label-correlation
sensitive loss function for recovering the predicted label outputs. Our C2AE is
achieved by integrating the DNN architectures of canonical correlation analysis
and autoencoder, which allows end-to-end learning and prediction with the
ability to exploit label dependency. Moreover, our C2AE can be easily extended
to address the learning problem with missing labels. Our experiments on
multiple datasets with different scales confirm the effectiveness and
robustness of our proposed method, which is shown to perform favorably against
state-of-the-art methods for multi-label classification.Comment: published in AAAI-201
Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm
Reverberation, which is generally caused by sound reflections from walls,
ceilings, and floors, can result in severe performance degradation of acoustic
applications. Due to a complicated combination of attenuation and time-delay
effects, the reverberation property is difficult to characterize, and it
remains a challenging task to effectively retrieve the anechoic speech signals
from reverberation ones. In the present study, we proposed a novel integrated
deep and ensemble learning algorithm (IDEA) for speech dereverberation. The
IDEA consists of offline and online phases. In the offline phase, we train
multiple dereverberation models, each aiming to precisely dereverb speech
signals in a particular acoustic environment; then a unified fusion function is
estimated that aims to integrate the information of multiple dereverberation
models. In the online phase, an input utterance is first processed by each of
the dereverberation models. The outputs of all models are integrated
accordingly to generate the final anechoic signal. We evaluated the IDEA on
designed acoustic environments, including both matched and mismatched
conditions of the training and testing data. Experimental results confirm that
the proposed IDEA outperforms single deep-neural-network-based dereverberation
model with the same model architecture and training data
A Web-Services-Based P2P Computing-Power Sharing Architecture
As demands of data processing and computing power are increasing, existing information system architectures become insufficient. Some organizations try to figure out how to keep their systems work without purchasing new hardware and software. Therefore, a Webservices-based model which shares the resource over the network like a P2P network will be proposed to meet this requirement in this paper. In addition, this paper also discusses some problems about security, motivation, flexibility, compatibility and workflow management for the traditional P2P power sharing models. Our new computing architecture - Computing Power Services (CPS) - will aim to address these problems. For the shortcomings about flexibility, compatibility and workflow management, CPS utilizes Web Services and Business Process Execution Language (BPEL) to overcome them. Because CPS is assumed to run in a reliable network where peers trust each other, the concerns about security and motivation will be negated. In essence, CPS is a lightweight Web-Services-based P2P power sharing environment and suitable for executing computing works in batch in a reliable networ
Light scattering properties beyond weak-field excitation in a few-atom system
In the study of optical properties of large atomic system, a weak laser
driving is often assumed to simplify the system dynamics by linearly coupled
equations. Here we investigate the light scattering properties of atomic
ensembles beyond weak-field excitation through cumulant expansion method. By
progressively incorporating higher-order correlations into the steady-state
equations, an enhanced accuracy can be achieved in comparison to the exact
solutions from solving a full density matrix. Our analysis reveals that, in the
regime of weak dipole-dipole interaction (DDI), the first-order expansion
yields satisfactory predictions for optical depth, while denser atomic
configurations necessitate consideration of higher-order correlations. As the
intensity of incident light increases, atom saturation effects become
noticeable, giving rise to significant changes of light transparency, energy
shift, and decay rate. This saturation phenomenon extends to subradiant atom
arrays even under weak driving conditions, leading to substantial deviations
from the linear model. Our findings demonstrate the potential of mean-field
models as good extensions to linear models as it balances both accuracy and
computational complexity, which can be an effective tool for probing optical
properties in large atom systems. However, the crucial role of higher-order
cumulants in large atom systems under finite laser field excitations remains
unclear since it is challenging theoretically owing to the
exponentially-increasing Hilbert space in such light-matter interacting
systems.Comment: 4 figure
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