Biogeochemistry of dimethylsulfide in the South China Sea

The distribution of dimethylsulfide (DMS) was studied in surface seawater and vertical profiles at nineteen stations in the Nansha Islands sea area of the South China Sea. The concentrations of DMS in surface-layer (0-1 m) seawater vary from 64 to 140 ng S/L with high values found in the productive regions, in agreement with the horizontal distribution of chlorophyll a. The vertical profiles of DMS show a single peak shape with maximum concentrations occurring at depths between 30-75 m. The DMS concentrations are correlated with chlorophyll a levels both in the upper 20 m of seawater as well as in vertical profiles. A clear diel variation in DMS concentration is observed at the 50-m water layer at a fixed station with the highest DMS concentration found in the late afternoon. The DMS concentrations are associated with environmental factors such as seawater temperature, dissolved O2 and nutrient contents. Although DMS is correlated to chlorophyll a, the phytoplankton species is a major factor responsible for the obviously higher DMS concentration than expected from the phytoplankton biomass in this sea area. The sea-to-air flux of DMS from this sea area is calculated to be 7.6 µmol m-2 d-1

Novel insights into bacterial dimethylsulfoniopropionate catabolism in the East China Sea

The compatible solute Dimethylsulfoniopropionate (DMSP), made by many marine organisms, is one of Earth’s most abundant organosulfur molecules. Many marine bacteria import DMSP and can degrade it as a source of carbon and/or sulfur via DMSP cleavage or DMSP demethylation pathways, which can generate the climate active gases dimethyl sulfide (DMS) or methanthiol (MeSH), respectively. Here we used culture-dependent and -independent methods to study bacteria catabolising DMSP in East China Sea (ECS). Of bacterial isolates, 42.11% showed DMSP-dependent DMS (Ddd+) activity, and 12.28% produced detectable levels of MeSH. Interestingly, although most Ddd+ isolates were Alphaproteobacteria (mainly Roseobacters), many gram-positive Actinobacteria were also shown to cleave DMSP producing DMS. The mechanism by which these Actinobacteria cleave DMSP is unknown, since no known functional ddd genes have been identified in genome sequences of Ddd+ Microbacterium and Agrococcus isolates or in any other sequenced Actinobacteria genomes. Gene probes to the DMSP demethylation gene dmdA and the DMSP lyase gene dddP demonstrated that these DMSP-degrading genes are abundant and widely distributed in ECS seawaters. dmdA was present in relatively high proportions in both surface (19.53% ± 6.70%) and bottom seawater bacteria (16.00% ± 8.73%). In contrast, dddP abundance positively correlated with chlorophyll a, and gradually decreased with the distance from land, which implies that the bacterial DMSP lyase gene dddP might be from bacterial groups that closely associate with phytoplankton. Bacterial community analysis showed positive correlations between Rhodobacteraceae abundance and concentrations of DMS and DMSP, further confirming the link between this abundant bacterial class and the environmental DMSP cycling

Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Deep learning (DL) and deep neural networks (DNNs) can learn the hidden features of data and build the classifier automatically for decision making, which have been widely used in the SEI research. Considering the insufficiently labeled training samples and large unlabeled training samples, semi-supervised learning-based SEI (SS-SEI) methods have been proposed. However, there are few SS-SEI methods focusing on extracting the discriminative and generalized semantic features of radio signals. In this paper, we propose an SS-SEI method using metric-adversarial training (MAT). Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open-source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset and WiFi dataset and is compared with state-of-the-art methods. The simulation results show that the proposed method achieves better identification performance than existing state-of-the-art methods. Specifically, when the ratio of the number of labeled training samples to the number of all training samples is 10\%, the identification accuracy is 84.80\% under the ADS-B dataset and 80.70\% under the WiFi dataset. Our code can be downloaded from https://github.com/lovelymimola/MAT-based-SS-SEI.Comment: 12 pages, 5 figures, Journa

Inhibition of nuclear factor-kappa B enhances the tumor growth of ovarian cancer cell line derived from a low-grade papillary serous carcinoma in p53-independent pathway

Background: NF-kB can function as an oncogene or tumor suppressor depending on cancer types. The role of NF-kB in low-grade serous ovarian cancer, however, has never been tested. We sought to elucidate the function of NF-kB in the low-grade serous ovarian cancer. Methods: The ovarian cancer cell line, HOC-7, derived from a low-grade papillary serous carcinoma. Introduction of a dominant negative mutant, IkBαM, which resulted in decrease of NF-kB function in ovarian cancer cell lines. The transcription ability, tumorigenesis, cell proliferation and apoptosis were observed in derivative cell lines in comparison with parental cells. Results: Western blot analysis indicated increased expression of the anti-apoptotic proteins Bcl-xL and reduced expression of the pro-apoptotic proteins Bax, Bad, and Bid in HOC-7/IĸBαM cell. Further investigations validate this conclusion in KRAS wildtype cell line SKOV3. Interesting, NF-kB can exert its pro-apoptotic effect by activating mitogen-activated protein kinase (MAPK) phosphorylation in SKOV3 ovarian cancer cell, whereas opposite changes detected in p-MEK in HOC-7 ovarian cancer cell, the same as some chemoresistant ovarian cancer cell lines. In vivo animal assay performed on BALB/athymic mice showed that injection of HOC-7 induced subcutaneous tumor growth, which was completely regressed within 7 weeks. In comparison, HOC-7/IĸBαM cells caused sustained tumor growth and abrogated tumor regression, suggesting that knock-down of NF-kB by IĸBαM promoted sustained tumor growth and delayed tumor regression in HOC-7 cells. Conclusion: Our results demonstrated that NF-kB may function as a tumor suppressor by facilitating regression of low grade ovarian serous carcinoma through activating pro-apoptotic pathways

Characterization of the complete mitochondrial genome and phylogenetic analysis of Pelodiscus sinensis, a mutant Chinese soft-shell turtle

The Chinese soft-shell turtle (Pelodiscus sinensis, Testudines: Pelodiscus) shows geographical variation, and one strain is the inked turtle. Wild population numbers have dropped substantially during the past decades, and the species is now classed as vulnerable. However, little genetic data exists so this study aimed to sequence and analyze the complete mitochondrial genome. The circular double-stranded genome is 17,145 bp in length and contains 13 protein-coding genes (PCGs), two rRNA genes, 22 tRNA genes, an L-strand replication origin and a control region. The base composition is 35.5% A, 27.3% T, 11.8% G and 25.4% C, with an AT content of 62.8%. Trionychidae species were divided into two clades based on phylogenetic analysis, and the closest genetic distance was between Trionyx axenaria and P. sinensis. This study provides basic genetic data for future studies on conservation biology, phylogenetic and evolutionary analysis of this inked strain of the Chinese soft-shell turtle.</p