ABSTRACT
The development of IoT technologies and social network services (SNS) are contributing to the growth of big data. However, the vast amount of data makes it difficult for users to find the information they need, and as a result, the demand for a system that provides the desired information in a well-organized form is increasing. Many studies are being conducted to extract desired information from data, and application studies such as automatic report generation are also being conducted. To generate a report for a given topic, a report generation system is required to extract essential information from big data and re-organize it in a compact form. Image selection system also plays an important role in automatic report generation as insertion of appropriate images can increase the completeness and readability of the report. In this study, we propose an image selection framework for recommending an appropriate image for a part of a report by combining textual information used in text-based image retrieval and visual features used in content-based image retrieval. In addition, the proposed image selection framework adopts an image filtering module that is specially designed for filtering out some images that are not suitable for use in reports. Through experiments on two datasets and comparative experiment with state-of-the-art work, we confirmed that our proposed method recommends images that fit the user's intention, and its practical applicability.
ABSTRACT
Opioids account for 69,000 overdose deaths per annum worldwide and cause serious side effects. Safer analgesics are urgently needed. The endogenous opioid peptide Leu-Enkephalin (Leu-ENK) is ineffective when introduced peripherally due to poor stability and limited membrane permeability. We developed a focused library of Leu-ENK analogs containing small hydrophobic modifications. N-pivaloyl analog KK-103 showed the highest binding affinity to the delta opioid receptor (68% relative to Leu-ENK) and an extended plasma half-life of 37 h. In the murine hot-plate model, subcutaneous KK-103 showed 10-fold improved anticonception (142%MPE·h) compared to Leu-ENK (14%MPE·h). In the formalin model, KK-103 reduced the licking and biting time to ~50% relative to the vehicle group. KK-103 was shown to act through the opioid receptors in the central nervous system. In contrast to morphine, KK-103 was longer-lasting and did not induce breathing depression, physical dependence, and tolerance, showing potential as a safe and effective analgesic.
