Identification and Modulation of the Key Amino Acid Residue Responsible for the pH Sensitivity of Neoculin, a Taste-Modifying Protein

Neoculin occurring in the tropical fruit of Curculigo latifolia is currently the only protein that possesses both a sweet taste and a taste-modifying activity of converting sourness into sweetness. Structurally, this protein is a heterodimer consisting of a neoculin acidic subunit (NAS) and a neoculin basic subunit (NBS). Recently, we found that a neoculin variant in which all five histidine residues are replaced with alanine elicits intense sweetness at both neutral and acidic pH but has no taste-modifying activity. To identify the critical histidine residue(s) responsible for this activity, we produced a series of His-to-Ala neoculin variants and evaluated their sweetness levels using cell-based calcium imaging and a human sensory test. Our results suggest that NBS His11 functions as a primary pH sensor for neoculin to elicit taste modification. Neoculin variants with substitutions other than His-to-Ala were further analyzed to clarify the role of the NBS position 11 in the taste-modifying activity. We found that the aromatic character of the amino acid side chain is necessary to elicit the pH-dependent sweetness. Interestingly, since the His-to-Tyr variant is a novel taste-modifying protein with alternative pH sensitivity, the position 11 in NBS can be critical to modulate the pH-dependent activity of neoculin. These findings are important for understanding the pH-sensitive functional changes in proteinaceous ligands in general and the interaction of taste receptor–taste substance in particular

The intertidal macrobenthic fauna of the Hatakejima Experimental Field, Wakayama Prefecture, Japan, in 2019

ファイル差し替え（2021-05-17）Hatakejima Experimental Field is located in Tanabe Bay, Wakayama Prefecture, Japan, which is composed of Hatakejima Island and Komarujima Islet, connected to the former in low tide. Hatakejima Island was purchased by Kyoto University and was designated as the “Hatakejima Experimental Field” in 1968. The year 2019 marks the 50th year of the long-term surveys that have been formally conducted on the experimental field since 1969 (i.e., the Century of Research Project). We conducted a field survey to record the macrobenthic fauna of the experimental field in 2019. A total of 168 species of 11 phyla were recorded in this survey. In each phylum, the number of species is listed as follows in descending order: Mollusca (78 spp.), Arthropoda (27 spp.), Echinodermata (23 spp.), Annelida (21 spp.), Cnidaria (7 spp.), Porifera (3 spp.), Nemertea (3 spp.), Platyhelminthes (2 spp.), Chordata (2 spp.), Bryozoa (1 sp.), and Hemichordata (1 sp.). We also recorded and discussed the influence of recent environmental changes around the Hatakejima Experimental Field. Tropical sea urchin species disappeared in the winter of 2017–2018 following the large meander of the Kuroshio Current, which led to decreasing water temperatures. The population of the seagrass Zostera japonica drastically decreased on the western sandy shore of the island in 2019, most likely because of two big typhoons in September 2018. We must conduct continuous observations to aid the recovery of seagrass-associated communities and protect the experimental field to keep high biodiversity of macrobenthic fauna in the future

Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''. Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. Analysis of 81 submissions from 31 teams revealed two remarkable types of domain generalization techniques: 1) domain-mixing-based approach that obtains generalized representations and 2) domain-classification-based approach that explicitly or implicitly classifies different domains to improve detection performance for each domain.Comment: arXiv admin note: substantial text overlap with arXiv:2106.0449