話し言葉解析に基づく話者知識の自動獲得

Conversation is one of the most effective ways for exchanging information, and it is very worth to share and reuse\ud the speaker’s knowledge. This paper proposes a method of information extraction from spoken conversations. The\ud method extracts task-oriented information from spoken utterances and acquires speaker’s knowledge by integrating\ud them. The utterance intention tags and dependency structures are used for selecting the utterances to be processed\ud and integrating the information linguistically, respectively. An extraction experiment using CIAIR in-car speech\ud corpus achieves a precision of 85.7% and a recall of 64.2%, and has shown the method to be effective

Size-Based Isolation of Circulating Tumor Cells in Lung Cancer Patients Using a Microcavity Array System

<div><p>Background</p><p>Epithelial cell adhesion molecule (EpCAM)-based enumeration of circulating tumor cells (CTC) has prognostic value in patients with solid tumors, such as advanced breast, colon, and prostate cancer. However, poor sensitivity has been reported for non-small cell lung cancer (NSCLC). To address this problem, we developed a microcavity array (MCA) system integrated with a miniaturized device for CTC isolation without relying on EpCAM expression. Here, we report the results of a clinical study on CTCs of advanced lung cancer patients in which we compared the MCA system with the CellSearch system, which employs the conventional EpCAM-based method.</p><p>Methods</p><p>Paired peripheral blood samples were collected from 43 metastatic lung cancer patients to enumerate CTCs using the CellSearch system according to the manufacturer’s protocol and the MCA system by immunolabeling and cytomorphological analysis. The presence of CTCs was assessed blindly and independently by both systems.</p><p>Results</p><p>CTCs were detected in 17 of 22 NSCLC patients using the MCA system versus 7 of 22 patients using the CellSearch system. On the other hand, CTCs were detected in 20 of 21 small cell lung cancer (SCLC) patients using the MCA system versus 12 of 21 patients using the CellSearch system. Significantly more CTCs in NSCLC patients were detected by the MCA system (median 13, range 0–291 cells/7.5 mL) than by the CellSearch system (median 0, range 0–37 cells/7.5 ml) demonstrating statistical superiority (p = 0.0015). Statistical significance was not reached in SCLC though the trend favoring the MCA system over the CellSearch system was observed (p = 0.2888). The MCA system also isolated CTC clusters from patients who had been identified as CTC negative using the CellSearch system.</p><p>Conclusions</p><p>The MCA system has a potential to isolate significantly more CTCs and CTC clusters in advanced lung cancer patients compared to the CellSearch system.</p></div

CTC recovery efficiency and average cell diameter.

<p>Cells were spiked into 1 mL of normal blood and recovered using the MCA system.</p

MCA system for size-based isolation of CTCs.

<p>(a) Schematic diagram of the structure of the MCA system. (b) Scanning electron microscope image of a cultured tumor cell line trapped on the MCA system. (c–f) Cells isolated from SCLC patient blood stained with Hoechst 33342 (c) and fluorescent-labeled antibodies that target cytokeratin (d) and CD45 (e). Merging of the images (f) allowed for identification of CTCs and hematologic cells. Scale bar = 60 µm.</p

Gallery of cells captured on the MCA from blood of advanced lung cancer patients.

<p>Cells were stained with Hoechst 33342, FITC-labeled anti-cytokeratin antibody, and PE-labeled anti-CD45 antibody.</p

CTC count using the MCA system.

<p>CTC count/7.5 mL blood is shown for 6 healthy donors, 22 NSCLC patients and 20 SCLC patients.</p

Gallery of CTCs captured on a transparent MCA from SCLC patient blood.

<p>May-Grünwald–Giemsa-stained cells showed a high nucleus–cytoplasm ratio and nuclear molding (×40). Black arrow indicates 9-µm microcavity. Scale bar = 60 µm.</p

Patient characteristics.

<p>Patient characteristics.</p