End-to-end dialogue structure parsing on multi-floor dialogue based on multi-task learning

A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor. In the multi-floor dialogue, at least one multi-communicating member who is a participant of multiple floors and coordinates each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, We proposed a neural dialogue structure parser with an attention mechanism that applies multi-task learning to automatically identify the dialogue structure of multi-floor dialogues in a collaborative robot navigation domain. Furthermore, we propose to use dialogue response prediction as an auxiliary objective of the multi-floor dialogue structure parser to enhance the consistency of the multi-floor dialogue structure parsing. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than conventional models in multi-floor dialogue

Imaging tools for mediastinal cystic lesions

Objective : To identify and differentiate patients with mediastinal cysts from those with cystic tumors requiring surgery. Methods : A total of 36 patients with mediastinal cystic lesions were enrolled. The patients were separated into two groups based on pathological findings : those with mediastinal cysts (n=23) and those with mediastinal tumors (n=13). The cystic components were measured using imaging parameters including mean computed tomography (CT) value, apparent diffusion coefficient (ADC), T1 signal intensity ratio (T1SI-ratio), and T2 signal intensity ratio (T2SI-ratio), acquired from magnetic resonance imaging (MRI) ; and standardized maximum uptake value (SUVmax) from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Both groups were statistically compared. Results : Comparative parameters between the cysts and tumors revealed the following ratios : CT value, 40.9±21.2 versus (vs) 24.8±12.9 (p = 0.019) ; SUVmax, 1.18±0.50 vs 4.32±3.52 (p = 0.003) ; ADC, 3.46±0.96 vs 2.68±0.74 (p = 0.022) ; T1SI-ratio, 1.06±0.60 vs 1.35±0.92 (p = 0.648) ; T2SI-ratio, 5.40±1.80 vs 4.33±1.58 (p = 0.194). However, there was no correlation between FDG uptake and ADC value. Conclusions : SUVmax from 18F-FDG PET/CT and ADC derived from MRI were effective in facilitating preoperative diagnosis to differentiate mediastinal cysts from tumors. However, these examinations may be complementary to one another, not dominant

Huge retroperitoneal dedifferentiated liposarcoma presented as acute pancreatitis : Report of a case

A 74-year-old male with abdominal pain was admitted to the emergency room in our hospital. The high value of serum amylase was shown in his blood test. The postcontrast computed tomography (CT) showed the huge retroperitoneal tumor with a thinwalled mass occupying most of the part of the right retroperitoneal space. The tumor spread into the soft tissues around the pancreas ; as a result, the duodenum was compressed and the pancreas was displaced to the right side. The irregular pancreatic outline, obliterated peripancreatic fatty tissue and fluid in the left anterior pararenal space were revealed, so acute pancreatitis was diagnosed. The diagnostic biopsy of retroperitoneal tumor was done, and the pathological findings of retroperitoneal mass revealed dedifferentiated liposarcoma. The medical treatment against acute pancreatitis was performed firstly. After the patient recovered from that, the surgical resection of the tumor with the right kidney and right adrenal gland was completed successfully. The patient remained well, without any evidence of recurrence three months after surgery. However, the histology showed dedifferentiated liposarcoma ; therefore, postoperative regular examination is necessary

Applicability of Preoperative Nuclear Morphometry to Evaluating Risk for Cervical Lymph Node Metastasis in Oral Squamous Cell Carcinoma

Background: We previously reported the utility of preoperative nuclear morphometry for evaluating risk for cervical lymph node metastases in tongue squamous cell carcinoma. The risk for lymph node metastasis in oral squamous cell carcinoma, however, is known to differ depending on the anatomical site of the primary tumor, such as the tongue, gingiva, mouth floor, and buccal mucosa. In this study, we evaluated the applicability of this morphometric technique to evaluating the risk for cervical lymph node metastasis in oral squamous cell carcinoma. Methods: A digital image system was used to measure the mean nuclear area, mean nuclear perimeter, nuclear circular rate, ratio of nuclear length to width (aspect ratio), and nuclear area coefficient of variation (NACV). Relationships between these parameters and nodal status were evaluated by t-test and logistic regression analysis. Results: Eighty-eight cases of squamous cell carcinoma (52 of the tongue, 25 of the gingiva, 4 of the buccal mucosa, and 7 of the mouth floor) were included: 46 with positive node classification and 42 with negative node classification. Nuclear area and perimeter were significantly larger in node-positive cases than in nodenegative cases; however, there were no significant differences in circular rate, aspect ratio, or NACV. We derived two risk models based on the results of multivariate analysis: Model 1, which identified age and mean nuclear area and Model 2, which identified age and mean nuclear perimeter. It should be noted that primary tumor site was not associated the pN-positive status. There were no significant differences in pathological nodal status by aspect ratio, NACV, or primary tumor site. Conclusion: Our method of preoperative nuclear morphometry may contribute valuable information to evaluations of the risk for lymph node metastasis in oral squamous cell carcinoma

Redox-controlled backbone dynamics of human cytochrome c revealed by 15N NMR relaxation measurements

Redox-controlled backbone dynamics in cytochrome c (Cyt c) were revealed by 2D 15N NMR relaxation experiments. 15N T1 and T2 values and 1H-15N NOEs of uniformly 15N-labeled reduced and oxidized Cyt c were measured, and the generalized order parameters (S2), the effective correlation time for internal motion (τe), the 15N exchange broadening contributions (Rex) for each residue, and the overall correlation time (τm) were estimated by model-free dynamics formalism. These dynamic parameters clearly showed that the backbone dynamics of Cyt c are highly restricted due to the covalently bound heme that functions as the stable hydrophobic core. Upon oxidation of the heme iron in Cyt c, the average S2 value was increased from 0.88 ± 0.01 to 0.92 ± 0.01, demonstrating that the mobility of the backbone is further restricted in the oxidized form. Such increases in the S-2 values were more prominent in the loop regions, including amino acid residues near the thioether bonds to the heme moiety and positively charged region around Lys87. Both of the regions are supposed to form the interaction site for cytochrome c oxidase (CcO) and the electron pathway from Cyt c to CcO. The redox-dependent mobility of the backbone in the interaction site for the electron transfer to CcO suggests an electron transfer mechanism regulated by the backbone dynamics in the Cyt c-CcO system

Controlled Neural Response Generation by Given Dialogue Acts Based on Label-aware Adversarial Learning

Building a controllable neural conversation model (NCM) is an important task. In this paper, we focus on controlling the responses of NCMs using dialogue act labels of responses as conditions. We introduce a reinforcement learning framework involving adversarial learning for conditional response generation. Our proposed method has a new label-aware objective that encourages the generation of discriminative responses by the given dialogue act label while maintaining the naturalness of the generated responses. We compared the proposed method with conventional methods that generate conditional responses. The experimental results showed that our proposed method has higher controllability conditioned by the dialogue acts even though it has higher or comparable naturalness to the conventional models

Entrainable Neural Conversation Model Based on Reinforcement Learning

The synchronization of words in conversation, called entrainment, is generally observed in human-human conversations. Entrainment has a high correlation with dialogue success, naturalness, and engagement. In this article, we de ne entrainment scores based on the word similarities in semantic space to evaluate the entrainment of system generation.We optimized a neural conversation model to the entrainment scores using reinforcement learning so that the system can control the degree of entrainment of the system response. Experimental results showed that the proposed entrainable neural conversation model generated comparable or more natural responses than conventional models and satisfactorily controlled the degree of entrainment of the generated responses