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The oceanic-to-neritic species shift of microzooplanktonic tintinnids and their interaction with relevant abiotic variables are two crucial processes in the marine ecosystem. However, these processes remain poorly documented in China's marginal seas. In the summer of 2022, we investigated the community structure of pelagic tintinnids in surface waters from the South China Sea (SCS) to the Yellow Sea (YS), passing through the East China Sea (ECS). A number of 58 species from 23 genera were identified, with 36 and 22 species belonging to oceanic and neritic genera, respectively. The abundance proportion of oceanic and neritic genera exhibited a decreasing and increasing trend, respectively, from the SCS to YS. Furthermore, four distinctive tintinnid community groups were classified based on cluster analysis using tintinnid species and abundance data, and the position of southern Taiwan Strait was identified as the “Shift Point” for oceanic-to-neritic species dominance. The top two tintinnid species in each group showed distinct variations in body size. Additionally, multivariate biotic-abiotic statistical analyses revealed that temperature determined tintinnid species richness, while temperature, salinity, Si(OH)4, and Chl a determined tintinnid abundance. Our study provides a substantial foundation for recognizing the oceanic-to-neritic species shift of tintinnids in the China's marginal seas, and highlights the role of biotic-abiotic factors in driving biogeochemical fluxes and the potential response of microzooplankton to future climate change. © 2023 Elsevier B.V.

The problem of extracting the user's favorite tuple from a large dataset attracts a lot of attention in the database community. Existing studies attempt to search for the target tuple with the help of user interaction. Specifically, they ask a user several questions, each of which consists of two tuples and asks the user to indicate which one s/he prefers. Based on the feedback, the user preference is learned implicitly and the target tuple w.r.t. the learned preference is returned. However, they mainly consider datasets with numerical attributes (e.g., price). In practice, tuples can also be described by categorical attributes (e.g., color), where there is no trivial order in the attribute values. Even if the categorical attributes can be reduced into numerical ones using conventional strategies (e.g., one-hot encoding), existing methods do not work well. In this paper, we study how to find the user's favorite tuple from datasets with mixed attributes (including both numerical and categorical attributes) by interacting with the user.We study our problem progressively. Firstly, we inquiry a special case in which tuples are only described by categorical attributes. We present algorithm SP-Tree that asks an asymptotically optimal number of questions. Secondly, we explore the general case in which tuples are described by numerical and categorical attributes. We propose algorithm GE-Graph that performs well theoretically and empirically. Experiments are conducted on synthetic and real datasets. The results show that our algorithms outperform existing ones on both the execution time and the number of questions asked. Under typical settings, we reduce dozens of questions asked and speed up by several orders of magnitude. © 2023 IEEE.

Longicatenamides A–D are cyclic hexapeptides isolated from the combined culture of Streptomyces sp. KUSC_F05 and Tsukamurella pulmonis TP-B0596. Because these peptides are not detected in the monoculture broth of the actinomycete, they are key tools for understanding chemical communication in the microbial world. Herein, we report the solid-phase total synthesis and structural confirmation of longicatenamide A. First, commercially unavailable building blocks were chemically synthesized with stereocontrol. Second, the peptide chain was elongated via Fmoc-based solid-phase peptide synthesis. Third, the peptide chain was cyclized in the solution phase, followed by simultaneous cleavage of all protecting groups to afford longicatenamide A. Chromatographic analysis corroborated the chemical structure of longicatenamide A. Furthermore, the antimicrobial activity of synthesized longicatenamide A was confirmed. The developed solid-phase synthesis is expected to facilitate the rapid synthesis of diverse synthetic analogues.

There are various queries proposed to assist users in finding their favorite tuples from a dataset with the help of user interaction. Specifically, they interact with a user by asking questions. Each question presents two tuples, which are selected from the dataset based on the user’s answers to the previous questions, and asks the user to select the one s/he prefers. Following the user feedback, the user preference is learned implicitly, and the best tuple w.r.t. the learned preference is returned. However, existing queries only consider datasets with ordered attributes (e.g., price), where there exists a trivial order on the attribute values. In practice, a dataset can also be described by unordered attributes, where there is no consensus about the order of the attribute values. For example, the size of a laptop is an unordered attribute. One user might favor a large size because s/he could enjoy a large screen, while another user may prefer a small size for portability. In this paper, we study how to find a user’s favorite tuple from the dataset that has both ordered and unordered attributes by interacting with the user. We study our problem progressively. First, we look into a special case in which the dataset is described by one ordered and one unordered attributes. We present algorithm DI that is asymptotically optimal in terms of the number of questions asked. Then, we dig into the general case in which the dataset has several ordered and unordered attributes. We propose two algorithms BS and EDI that have provable performance guarantees and perform well empirically. Experiments were conducted on synthetic and real datasets, showing that our algorithms outperform existing algorithms in the number of questions asked and the execution time. Under typical settings, our algorithms ask up to 10 times fewer questions and take several orders of magnitude less time than existing algorithms. © 2022, VLDB Endowment. All rights reserved.