162 research outputs found
中国法における重大な錯誤と法律行為の無効:一般条項の具体化
Tohoku University渡辺達徳課
Small Interference RNA Targeting TLR4 Gene Effectively Attenuates Pulmonary Inflammation in a Rat Model
Objective. The present study was to investigate the feasibility of adenovirus-mediated small interference RNA (siRNA) targeting Toll-like receptor 4 (TLR4) gene in ameliorating lipopolysaccharide- (LPS-) induced acute lung injury (ALI). Methods. In vitro, alveolar macrophages (AMs) were treated with Ad-siTLR4 and Ad-EFGP, respectively, for 12 h, 24 h, and 48 h, and then with LPS (100 ng/mL) for 2 h, and the function and expression of TLR4 were evaluated. In vivo, rats received intratracheal injection of 300 μL of normal saline (control group), 300 μL of Ad-EGFP (Ad-EGFP group), or 300 μL of Ad-siTLR4 (Ad-siTLR4 group) and then were intravenously treated with LPS (50 mg/kg) to induce ALI. Results. Ad-siTLR4 treatment significantly reduced TLR4 expression and production of proinflammatory cytokines following LPS treatment both in vitro and in vivo. Significant alleviation of tissue edema, microvascular protein leakage, and neutrophil infiltration was observed in the AdsiTLR4-treated animals. Conclusion. TLR4 plays a critical role in LPS-induced ALI, and transfection of Ad-siTLR4 can effectively downregulate TLR4 expression in vitro and in vivo, accompanied by alleviation of LPS-induced lung injury. These findings suggest that TLR4 may serve as a potential target in the treatment of ALI and RNA interfering targeting TLR4 expression represents a therapeutic strategy
The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis
Long-tailed relation classification is a challenging problem as the head
classes may dominate the training phase, thereby leading to the deterioration
of the tail performance. Existing solutions usually address this issue via
class-balancing strategies, e.g., data re-sampling and loss re-weighting, but
all these methods adhere to the schema of entangling learning of the
representation and classifier. In this study, we conduct an in-depth empirical
investigation into the long-tailed problem and found that pre-trained models
with instance-balanced sampling already capture the well-learned
representations for all classes; moreover, it is possible to achieve better
long-tailed classification ability at low cost by only adjusting the
classifier. Inspired by this observation, we propose a robust classifier with
attentive relation routing, which assigns soft weights by automatically
aggregating the relations. Extensive experiments on two datasets demonstrate
the effectiveness of our proposed approach. Code and datasets are available in
https://github.com/zjunlp/deepke
Design of Highly Isolated Compact Antenna Array for MIMO Applications
In order to achieve very high data rates in both the uplink and downlink channels, the multiple antenna systems are used within the mobile terminal as well as the base station of the future generation of mobile networks. When implemented in a size limited platform, the multiple antenna arrays suffer from strong mutual coupling between closely spaced array elements. In this paper, a rigorous procedure for the design of a 4-port compact planar antenna array with high port isolation is presented. The proposed design involves a decoupling network consisting of reactive elements, whose values can be obtained by the method of eigenmode analysis. Numerical results show the effectiveness of the proposed design approach in improving the port isolation of a compact four-element planar array
KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text Understanding
Deep text understanding, which requires the connections between a given
document and prior knowledge beyond its text, has been highlighted by many
benchmarks in recent years. However, these benchmarks have encountered two
major limitations. On the one hand, most of them require human annotation of
knowledge, which leads to limited knowledge coverage. On the other hand, they
usually use choices or spans in the texts as the answers, which results in
narrow answer space. To overcome these limitations, we build a new challenging
benchmark named KoRc in this paper. Compared with previous benchmarks, KoRC has
two advantages, i.e., broad knowledge coverage and flexible answer format.
Specifically, we utilize massive knowledge bases to guide annotators or large
language models (LLMs) to construct knowledgable questions. Moreover, we use
labels in knowledge bases rather than spans or choices as the final answers. We
test state-of-the-art models on KoRC and the experimental results show that the
strongest baseline only achieves 68.3% and 30.0% F1 measure in the
in-distribution and out-of-distribution test set, respectively. These results
indicate that deep text understanding is still an unsolved challenge. The
benchmark dataset, leaderboard, and baseline methods are released in
https://github.com/THU-KEG/KoRC
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