Metagenomic Sequencing Identifies Highly Diverse Assemblages of Dinoflagellate Cysts in Sediments From Ships\u27 Ballast Tanks

Ships\u27 ballast tanks have long been known as vectors for the introduction of organisms. We applied next-generation sequencing to detect dinoflagellates (mainly as cysts) in 32 ballast tank sediments collected during 2001-2003 from ships entering the Great Lakes or Chesapeake Bay and subsequently archived. Seventy-three dinoflagellates were fully identified to species level by this metagenomic approach and single-cell polymerase chain reaction (PCR)-based sequencing, including 19 toxic species, 36 harmful algal bloom (HAB) forming species, 22 previously unreported as producing cysts, and 55 reported from ballast tank sediments for the first time (including 13 freshwater species), plus 545 operational taxonomic units (OTUs) not fully identified due to a lack of reference sequences, indicating tank sediments are repositories of many previously undocumented taxa. Analyses indicated great heterogeneity of species composition among samples from different sources. Light and scanning electron microscopy and single-cell PCR sequencing supported and confirmed results of the metagenomic approach. This study increases the number of fully identified dinoflagellate species from ballast tank sediments to 142 (\u3e 50% increase). From the perspective of ballast water management, the high diversity and spatiotemporal heterogeneity of dinoflagellates in ballast tanks argues for continuing research and stringent adherence to procedures intended to prevent unintended introduction of non-indigenous toxic and HAB-forming species

Deep learning based channel estimation method for mine OFDM system

Abstract In this paper, we present a channel estimation approach based on deep learning to solve the problem that the orthogonal frequency division multiplexing (OFDM) system channel estimation algorithm cannot accurately obtain the channel state information in the complex environment of the mine, resulting in system performance degradation. First, LS channel estimation matrix is considered as a low-resolution image and the actual channel state information is considered as a high-resolution image. Then the optimization of the LS channel estimation matrix is achieved by the FSRCNN image super-resolution algorithm. We validate the effectiveness of the proposed algorithm by conducting experiments in different channel environments, different number of pilots, and mismatched signal-to-noise ratio scenarios. The simulation results show that the proposed scheme is much better than the traditional LS channel estimation method and the DFT-LS channel estimation method, and the accuracy of the proposed scheme approaches that of the MMSE channel estimation method when the number of pilots is low

Mine MIMO Depth Receiver: An Intelligent Receiving Model Based on Densely Connected Convolutional Networks

Multiple-input multiple-output (MIMO) systems suffer from high BER in the mining environment. In this paper, the mine MIMO depth receiver model is proposed. The model uses densely connected convolutional networks for feature extraction and constructs multiple binary classifiers to recover the original information. Compared with conventional MIMO receivers, the model has no error accumulation caused by processes such as decoding and demodulation. The experimental results show that the model has better performance than conventional decoding methods under different modulation codes and variations in the number of transmitting terminals. Furthermore, we demonstrate that the model can still achieve effective decoding and recover the original information with some data loss at the receiver

<i style="mso-bidi-font-style:normal"><span style="font-size:11.0pt;font-family:"Times New Roman";mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:Mangal;mso-ansi-language:EN-GB; mso-fareast-language:EN-US;mso-bidi-language:HI" lang="EN-GB">Gracilariopsis longissima</span></i><span style="font-size:11.0pt;font-family:"Times New Roman";mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:Mangal;mso-ansi-language:EN-GB; mso-fareast-language:EN-US;mso-bidi-language:HI" lang="EN-GB"> as biofilter for an Integrated Multi-Trophic aquaculture (IMTA) system with <i style="mso-bidi-font-style: normal">Sciaenops ocellatus</i>: Bioremediation efficiency and production in a recirculating system</span>

528-537A recirculating integrated system producing Sciaenops ocellatus and Gracilariopsis longissima (a red seaweed) was evaluated with respect to nutrient removal efficiency and production. G. longissima was found to be efficient in removing toxic ammonia and maintaining the water quality within an acceptable level for S. ocellatus culture. Specific growth rate (SGR) of S. ocellatus ranged from 0.064 ± 0.006% d-1 to 0.099% ± 0.010% d-1. Survival rates of S. ocellatus were 100% in the Integrated Multi-Trophic Aquaculture (IMTA) systems. <i style="mso-bidi-font-style: normal">G. longissima had average SGRs of 3.03 ± 0.11% d-1, 2.48 ± 0.04% d-1, 1.86 ± 0.26% d-1 and 1.12 ± 0.16% d-1 under initial densities of 1 g L-1, 3 g L-1, 6 g L-1 and 9 g L-1, respectively. Daily average nitrogen and phosphorus uptake rates of G. longissima were negatively correlated to cultivation densities in the recirculating system. Biofiltration capacity of G. longissima was confirmed by significantly reduced concentrations of ammonia, nitrate, nitrite and phosphate in the integrated system with S. ocellatus. Results indicated that G. longissima is suitable as a good candidate for IMTA systems

Highly Efficient Electrochemiluminescence Resonance Energy Transfer System in One Nanostructure: Its Application for Ultrasensitive Detection of MicroRNA in Cancer Cells

The electrochemiluminesce (ECL) efficiency of luminous emitter can be enhanced by the means of electrochemiluminesce resonance energy transfer (ECL-RET) with a matched donor. However, generally, the donor and acceptor pairs were separated in different independent nanostructures, experiencing the challenging issues of limited energy transfer efficiency and luminous stability. Herein, we designed novel ECL-RET model within one nanostructure containing the donor of tris­(4,4′-dicarboxylicacid-2,2′-bipyridyl) ruthenium­(II) dichloride (Ru­(dcbpy)₃²⁺) and the acceptor of CdSe@ZnS quantum dots (QDs) for acting as the ECL emitter (QDs-Ru­(dcbpy)₃²⁺), which significantly reduced the energy loss and improved the ECL efficiency of QDs because of the short path of energy transmission. To demonstrate the proof-of-concept, the proposed QDs-Ru­(dcbpy)₃²⁺ was employed to construct a new kind of ECL biosensor that could achieve the ultrasensitive detection of microRNA-141 (miRNA-141) combining target recycling amplification and the double-output conversion strategies. Notably, the proposed double-output conversion strategy enabled a small number of miRNA to be successfully transferred into a large number of reporter DNA which could capture numerous QDs-Ru­(dcbpy)₃²⁺-labeled signal probes on the sensing surface to realize the ECL response to the logarithm of the concentration of miRNA-141. With the ultrahigh-efficient ECL-RET in one nanostructure and the dual amplification including target recycling as well as double-output conversion strategies, the proposed biosensor realized ultrasensitive detection of miRNA-141 and performed the concentration range from 100 aM to 10 pM and the estimated detection limit was 33 aM (<i>S</i>/<i>N</i> = 3). Impressively, this method can sensitively detect the miRNA-141 of human prostate cancer cells and provide a significant boost for the detection of other biomarkers in early cancer diagnosis and therapeutic monitoring

Mitochondrial dynamics quantitatively revealed by STED nanoscopy with an enhanced squaraine variant probe

Live cell imaging of mitochondrial cristae is challenged by the unsuitability of current fluorescent probes and high phototoxicity. Here the authors develop a squarine variant probe (MitoESq-635) that is capable of longitudinal imaging of cristae with STED with minimal phototoxicity