The marine mollusk Aplysia is a useful model organism for studying the cellular bases of behavior and plasticity. However, molecular studies of Aplysia have been limited by the lack of genomic information. Recently, a large scale characterization of neuronal transcripts was performed in A. californica. Here, we report the analysis of a parallel set of neuronal transcripts from a closely related species A. kurodai found in the northwestern Pacific. We collected 4,859 nonredundant sequences from the nervous system tissue of A. kurodai. By performing microarray and real-time PCR analyses, we found that ApC/EBP, matrilin, antistasin, and eIF3e clones were significantly up-regulated and a BAT1 homologous clone was significantly down-regulated by 5-HT treatment. Among these, we further demonstrated that the Ap-eIF3e plays a key role in 5-HT-induced long-term facilitation (LTF) as a positive regulator.

The marine mollusk Aplysia is a useful model organism for studying the cellular bases of behavior and plasticity. However, molecular studies of Aplysia have been limited by the lack of genomic information. Recently, a large scale characterization of neuronal transcripts was performed in A. californica. Here, we report the analysis of a parallel set of neuronal transcripts from a closely related species A. kurodai found in the northwestern Pacific. We collected 4,859 nonredundant sequences from the nervous system tissue of A. kurodai. By performing microarray and real-time PCR analyses, we found that ApC/EBP, matrilin, antistasin, and eIF3e clones were significantly up-regulated and a BAT1 homologous clone was significantly down-regulated by 5-HT treatment. Among these, we further demonstrated that the Ap-eIF3e plays a key role in 5-HT-induced long-term facilitation (LTF) as a positive regulator.

Strong evidence exists for polyploidy having occurred during the evolution of the tribe Brassiceae. We show evidence for the dynamic and ongoing diploidization process by comparative analysis of the sequences of four paralogous Brassica rapa BAC clones and the homologous 124-kb segment of Arabidopsis thaliana chromosome 5. We estimated the times since divergence of the paralogous and homologous lineages. The three paralogous subgenomes of B. rapa triplicated 13 to 17 million years ago (MYA), very soon after the Arabidopsis and Brassica divergence occurred at 17 to 18 MYA. In addition, a pair of BACs represents a more recent segmental duplication, which occurred approximately 0.8 MYA, and provides an exception to the general expectation of three paralogous segments within the B. rapa genome. The Brassica genome segments show extensive interspersed gene loss relative to the inferred structure of the ancestral genome, whereas the Arabidopsis genome segment appears little changed. Representatives of all 32 genes in the Arabidopsis genome segment are represented in Brassica, but the hexaploid complement of 96 has been reduced to 54 in the three subgenomes, with compression of the genomic region lengths they occupy to between 52 and 110 kb. The gene content of the recently duplicated B. rapa genome segments is identical, but intergenic sequences differ.

Invasive freshwater turtles are major drivers of biodiversity loss, underscoring the importance of early detection and management. However, it is challenging for experts to manually monitor a broad geographic area, necessitating support tools. Deep learning-based object detection models have displayed high performance in automating wildlife monitoring tasks. Furthermore, hyperparameter optimization, including optimizer selection and hyperparameter tuning, might further enhance performance by optimizing training settings to the dataset. In this study, an optimized model was developed to apply hyperparameter optimization to detect and classify six invasive turtle species in Korea from images. The optimized model was compared to a default model trained using the default optimizer and hyperparameters. The optimized model outperformed the default model, as indicated by the evaluations of mean average precision using a fixed intersection over union threshold of 0.5 (0.973 vs. 0.959) and a range of thresholds ranging from 0.5 to 0.95 (0.841 vs. 0.815). The classification accuracy of the optimized model reached 92.7%, exceeding that of the default model (89.9%). These findings highlight the utility of hyperparameter optimization and suggest that the proposed approach can support the early detection of invasive turtles, thereby enhancing to invasive species management.

As the use of certain brominated flame retardants (BFRs) has been globally banned due to their high toxicity to biota, organophosphate flame retardants (OPFRs) have been proposed as alternatives. Currently, OPFRs are ubiquitously found in the ocean at levels comparable to those of BFRs; however, information on the comparative toxicities of BFRs and OPFRs to marine biota remains limited. In this study, we investigated the toxic effects of aryl and halogenatedOPFRs, triphenyl phosphate (TPhP) and tris(1,3-dichloro-2-propyl) phosphate (TDCPP), on the brackish water flea Diaphanosoma celebensis at the in vivo and molecular levels and compared them with those of a typical BFR, hexabromocyclododecane (HBCDD). Moreover, we investigated the effects of TPhP on transcriptomic modulation to understand the underlying mechanisms of toxicity. The results showed that all three FRs negatively affected reproduction, the antioxidant system, and cellular energy content; however, the effects were most pronounced in the TPhP-exposed group. Transcriptomic analysis revealed that genes involved in genetic processes, immune system, and nervous system were significantly modulated by TPhP exposure. These findings suggest that some OPFRs may be as toxic as BFRs and should be used with caution.

본 논문에서는 공급계통 및 정수시설에서 잔류염소 변화에 대해 모니터링하여 원수 수질 계측값과 공정별 수온, 잔류염소, 유량 등의 데이터를 분석 및 학습하여 인공지능화된 시스템이 결정하고 자동으로 투입할 수 있는 연구를 수행하였다. 결측치와 이상치를 제거하는 전처리 과정을 수행하고 주입률에 영향을 주는 변수를 선택한 후 변수사이 상관관계 분석을 하여 학습 결과의 피드백과 모형의 튜닝을 반복적으로 수행하여 가장 예측률이 높은 모델로서 GRU 모델을 적용하였고, 예측률이 97.27%의 좋은 성능이 있는 모델을 구축하였다. 본 연구에서 개발한 인공지능 기반 염소주입률 예측 모델은 각 공정에서 소독능이 유지되도록 주입률 예측 모델을 개발하였고, 환경변화에 따른 감소량을 예측하여 피드백 할 수 있도록 구현하였다. 이 모델 적용을 통해 수분석 시간과 휴먼 오류 등을 줄여 수질 기준에 알맞는 일정하게 소독능을 유지하는 고품질의 물 생산이 가능하다.