Homogeneous point mutation detection by quantum dot-mediated two-color fluorescence coincidence analysis

This report describes a new genotyping method capable of detecting low-abundant point mutations in a homogeneous, separation-free format. The method is based on integration of oligonucleotide ligation with a semiconductor quantum dot (QD)-mediated two-color fluorescence coincidence detection scheme. Surface-functionalized QDs are used to capture fluorophore-labeled ligation products, forming QD-oligonucleotide nanoassemblies. The presence of such nanoassemblies and thereby the genotype of the sample is determined by detecting the simultaneous emissions of QDs and fluorophores that occurs whenever a single nanoassembly flows through the femtoliter measurement volume of a confocal fluorescence detection system. The ability of this method to detect single events enables analysis of target signals with a multiple-parameter (intensities and count rates of the digitized target signals) approach to enhance assay sensitivity and specificity. We demonstrate that this new method is capable of detecting zeptomoles of targets and achieve an allele discrimination selectivity factor >10(5)

Microwave characteristics of liquid-crystal tunable capacitors

[[abstract]]This letter investigates the microwave characteristics of the liquid crystal tunable capacitors for the first time. With the dielectric anisotropy properties, the liquid crystal capacitors present very different characteristics compared to the semiconductor or MEMS tunable capacitors. A quality factor of 310 with a control voltage of 5 V was achieved at 4 GHz. A tuning range of 25.3% for the control voltages from 0 to 5 V was obtained at 5 GHz. The results demonstrate the potential applications of liquid crystals as dielectric materials for capacitors with high quality factors and wide tuning ranges at high frequencies, particularly suitable for the future flexible electronics with transparent substrates.[[fileno]]2030121010007[[department]]電機工程學

Investigating Knowledge Management Activities and Influential Factors of Contract Research Organizations (CRO)

The pharmaceutical industry is critical to a nation’s economic development and welfare. However, most pharmaceutical companies do not have the capabilities to complete clinical trials by themselves and need assistance from the contract/clinical research organization (CRO). Clinical trials are highly knowledge-intensive and include several fields, such as toxicology, statistics, production, biology, health care, pharmacology, trial protocol design, and legal regulation. In academic research, few studies have focused on studying this important area from the perspective of knowledge management. Consequently, this research aims to fill this research gap by investigating knowledge management activities and influencing factors of CRO. A holistic framework was designed for this research, with the former (knowledge management activities) including four major constructs: knowledge creation and absorption, knowledge accumulation and storage, knowledge flow and diffusion, and knowledge protection, and the latter (influencing factors) including strategy and leadership, organizational culture, people, and information technology. Four CROs in Taiwan were selected for in-depth case studies. The research results are expected to contribute to both academia and industry

Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation

With the rapid proliferation of online media sources and published news, headlines have become increasingly important for attracting readers to news articles, since users may be overwhelmed with the massive information. In this paper, we generate inspired headlines that preserve the nature of news articles and catch the eye of the reader simultaneously. The task of inspired headline generation can be viewed as a specific form of Headline Generation (HG) task, with the emphasis on creating an attractive headline from a given news article. To generate inspired headlines, we propose a novel framework called POpularity-Reinforced Learning for inspired Headline Generation (PORL-HG). PORL-HG exploits the extractive-abstractive architecture with 1) Popular Topic Attention (PTA) for guiding the extractor to select the attractive sentence from the article and 2) a popularity predictor for guiding the abstractor to rewrite the attractive sentence. Moreover, since the sentence selection of the extractor is not differentiable, techniques of reinforcement learning (RL) are utilized to bridge the gap with rewards obtained from a popularity score predictor. Through quantitative and qualitative experiments, we show that the proposed PORL-HG significantly outperforms the state-of-the-art headline generation models in terms of attractiveness evaluated by both human (71.03%) and the predictor (at least 27.60%), while the faithfulness of PORL-HG is also comparable to the state-of-the-art generation model.Comment: AAAI 202

CFEVER: A Chinese Fact Extraction and VERification Dataset

We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as "Supports", "Refutes", or "Not Enough Info" to depict its degree of factualness. Similar to the FEVER dataset, claims in the "Supports" and "Refutes" categories are also annotated with corresponding evidence sentences sourced from single or multiple pages in Chinese Wikipedia. Our labeled dataset holds a Fleiss' kappa value of 0.7934 for five-way inter-annotator agreement. In addition, through the experiments with the state-of-the-art approaches developed on the FEVER dataset and a simple baseline for CFEVER, we demonstrate that our dataset is a new rigorous benchmark for factual extraction and verification, which can be further used for developing automated systems to alleviate human fact-checking efforts. CFEVER is available at https://ikmlab.github.io/CFEVER.Comment: AAAI-2