Bipartite-play Dialogue Collection for Practical Automatic Evaluation of Dialogue Systems

Automation of dialogue system evaluation is a driving force for the efficient development of dialogue systems. This paper introduces the bipartite-play method, a dialogue collection method for automating dialogue system evaluation. It addresses the limitations of existing dialogue collection methods: (i) inability to compare with systems that are not publicly available, and (ii) vulnerability to cheating by intentionally selecting systems to be compared. Experimental results show that the automatic evaluation using the bipartite-play method mitigates these two drawbacks and correlates as strongly with human subjectivity as existing methods.Comment: 9 pages, Accepted to The AACL-IJCNLP 2022 Student Research Workshop (SRW

A Microliter-Scale High-throughput Screening System with Quantum-Dot Nanoprobes for Amyloid-β Aggregation Inhibitors

The aggregation of amyloid β protein (Aβ) is a key step in the pathogenesis of Alzheimer’s disease (AD), and therefore inhibitory substances for Aβ aggregation may have preventive and/or therapeutic potential for AD. Here we report a novel microliter-scale high-throughput screening system for Aβ aggregation inhibitors based on fluorescence microscopy-imaging technology with quantum-dot Nanoprobes. This screening system could be analyzed with a 5-µl sample volume when a 1536-well plate was used, and the inhibitory activity could be estimated as half-maximal effective concentrations (EC50). We attempted to comprehensively screen Aβ aggregation inhibitors from 52 spices using this system to assess whether this novel screening system is actually useful for screening inhibitors. Screening results indicate that approximately 90% of the ethanolic extracts from the spices showed inhibitory activity for Aβ aggregation. Interestingly, spices belonging to the Lamiaceae, the mint family, showed significantly higher activity than the average of tested spices. Furthermore, we tried to isolate the main inhibitory compound from Satureja hortensis, summer savory, a member of the Lamiaceae, using this system, and revealed that the main active compound was rosmarinic acid. These results demonstrate that this novel microliter-scale high-throughput screening system could be applied to the actual screening of Aβ aggregation inhibitors. Since this system can analyze at a microscopic scale, it is likely that further minimization of the system would easily be possible such as protein microarray technology

Crystal structures of two tropinone reductases: Different reaction stereospecificities in the same protein fold. (MOLECULAR BIOFUNCTION-Functional Molecular Conversion)

A pair of tropinone reductases (TRs) share 64% identical amino acid residues, and belong to the shortchain dehydrogenase/reductase family. In the synthesis of tropane alkaloids in several medicinal plants, the TRs reduce a carbonyl group of an alkaloid intermediate, tropinone, to hydroxy groups having different diastereomeric configurations. To clarify the structural basis for their different reaction stereospecificities, we determined the crystal structures of the two enzymes at 2.4- and 2.3-A resolutions. The overall folding of the two enzymes was almost identical. The substrate binding site was composed mostly of hydrophobic amino acids in both TRs, but the presence of different charged residues conferred different electrostatic environments on the two enzymes

Auxiliary self-supervision to metric learning for music similarity-based retrieval and auto-tagging.

In the realm of music information retrieval, similarity-based retrieval and auto-tagging serve as essential components. Similarity-based retrieval involves automatically analyzing a music track and fetching analogous tracks from a database. Auto-tagging, on the other hand, assesses a music track to deduce associated tags, such as genre and mood. Given the limitations and non-scalability of human supervision signals, it becomes crucial for models to learn from alternative sources to enhance their performance. Contrastive learning-based self-supervised learning, which exclusively relies on learning signals derived from music audio data, has demonstrated its efficacy in the context of auto-tagging. In this work, we propose a model that builds on the self-supervised learning approach to address the similarity-based retrieval challenge by introducing our method of metric learning with a self-supervised auxiliary loss. Furthermore, diverging from conventional self-supervised learning methodologies, we discovered the advantages of concurrently training the model with both self-supervision and supervision signals, without freezing pre-trained models. We also found that refraining from employing augmentation during the fine-tuning phase yields better results. Our experimental results confirm that the proposed methodology enhances retrieval and tagging performance metrics in two distinct scenarios: one where human-annotated tags are consistently available for all music tracks, and another where such tags are accessible only for a subset of music tracks

A microliter-scale high-throughput screening system with quantum-dot nanoprobes for amyloid-β aggregation inhibitors.

The aggregation of amyloid β protein (Aβ) is a key step in the pathogenesis of Alzheimer's disease (AD), and therefore inhibitory substances for Aβ aggregation may have preventive and/or therapeutic potential for AD. Here we report a novel microliter-scale high-throughput screening system for Aβ aggregation inhibitors based on fluorescence microscopy-imaging technology with quantum-dot Nanoprobes. This screening system could be analyzed with a 5-µl sample volume when a 1536-well plate was used, and the inhibitory activity could be estimated as half-maximal effective concentrations (EC50). We attempted to comprehensively screen Aβ aggregation inhibitors from 52 spices using this system to assess whether this novel screening system is actually useful for screening inhibitors. Screening results indicate that approximately 90% of the ethanolic extracts from the spices showed inhibitory activity for Aβ aggregation. Interestingly, spices belonging to the Lamiaceae, the mint family, showed significantly higher activity than the average of tested spices. Furthermore, we tried to isolate the main inhibitory compound from Saturejahortensis, summer savory, a member of the Lamiaceae, using this system, and revealed that the main active compound was rosmarinic acid. These results demonstrate that this novel microliter-scale high-throughput screening system could be applied to the actual screening of Aβ aggregation inhibitors. Since this system can analyze at a microscopic scale, it is likely that further minimization of the system would easily be possible such as protein microarray technology

Time-dependent Aβ aggregation.

<p>(A) 30 nM QDAβ and 30 µM Aβ₄₂ were incubated in a 1536-well plate at 37 ^°C, and observed over time by an inverted fluorescence microscope using a 4x objective. All images show the same field of a well. (B) Variations of fluorescence intensities of 10,000 pixels (100 × 100 pixel) in the center region of micrographs were estimated as SD values, the mean values were plotted against incubation time periods. Error bars represent ±SDs of the mean values of fluorescence intensities (n=3 separate experiments).</p

Isolation and identification of active compound from EtOH extract of summer savory.

<p>(A) A flow diagram of isolation steps. (B) Inhibition curves of isolated RA from summer savory (squares) and standard RA (triangles) were determined by the microliter-scale high-throughput screening (MHS) system (B, top) and the ThT assay (B, bottom). Vertical axes of the MHS system and the ThT assay are the percentage of average SD values and the percentage of average fluorescence intensity (FI) values, respectively. The EC₅₀ values of isolated RA and standard RA determined by the MHS system were 9.6 ± 0.1 and 11 ± 2 µM, respectively. In contrast to that, the EC₅₀ values of isolated RA and standard RA determined by ThT assay were 8.6 ± 0.8 and 6.3 ± 1.5 µM, respectively. Error bars represent ±SDs (n=3 separate experiments).</p