Effect of wind speed on the size distribution of gel particles in the sea surface microlayer: insights from a wind–wave channel experiment

Gel particles, such as transparent exopolymer particles (TEP) and Coomassie stainable particles (CSP), are important organic components in the sea surface microlayer (SML). Here, we present results on the effect of different wind speeds on the accumulation and size distribution of TEP and CSP during a wind wave channel experiment in the Aeolotron. Total areas of TEP (TEPSML) and CSP (CSPSML) in the surface microlayer were exponentially related to wind speed. At wind speeds    8 m s−1. Wind speeds  >  8 m s−1 also significantly altered the size distribution of TEPSML in the 2–16 µm size range towards smaller sizes. The response of the CSPSML size distribution to wind speed varied through time depending on the biogenic source of gels. Wind speeds  >  8 m s−1 decreased the slope of CSPSML size distribution significantly in the absence of autotrophic growth. For the slopes of TEP and CSP size distribution in the bulk water, no significant difference was observed between high and low wind speeds. Changes in spectral slopes between high and low wind speed were higher for TEPSML than for CSPSML, indicating that the impact of wind speed on size distribution of gel particles in the SML may be more pronounced for TEP than for CSP, and that CSPSML are less prone to aggregation during the low wind speeds. Addition of an E. huxleyi culture resulted in a higher contribution of submicron gels (0.4–1 µm) in the SML at higher wind speed ( >  6 m s−1), indicating that phytoplankton growth may potentially support the emission of submicron gels with sea spray aerosol

Comunidad de cianobacterias en los sedimentos del estuario del río Perla en China

Cyanobacterial community diversity in the sediment of the Pearl River Estuary in China was evaluated in this study by denaturing gradient gel electrophoresis (DGGE) during the wet and dry seasons. Nucleotide sequences obtained from DGGE bands were classified into five cyanobacterial clusters, including Synechococcus, Cyanobium, Chroococcus, Prochlorales and Tolypothrix. Synechococcus was identified as the dominant cyanobacterial group in the sediment samples; its distribution varied from the inner estuary to the outer estuary, with a wide range of salinity adaptation. Observed patterns of cyanobacterial communities changed markedly between sampling sites and seasons, suggesting that most cyanobacteria were not delivered via fresh water. Canonical correspondence analysis was conducted to determine the relationship between environmental variables and bacterial community structures during the dry season. The results suggested that the cyanobacterial community was significantly influenced by pH, salinity, PO4-P and NO3-N in sediments.La diversidad de la comunidad de cianobacterias en el sedimento del estuario del río Perla en China fue evaluada en este estudio por electroforesis en gel de gradiente desnaturalizante (DGGE) durante las estaciones húmeda y seca. Las secuencias de nucleótidos obtenidas de bandas DGGE se clasificaron en cinco grupos de cianobacterias, incluyendo Synechococcus, Cyanobium, Chroococcus, Prochlorales y Tolypothrix. Synechococcus fue identificado como el grupo dominante de cianobacterias en las muestras de sedimento, su distribución varió desde la parte interna del estuario hasta la externa, con un amplio rango de adaptación a la salinidad. Los patrones observados de las comunidades de cianobacterias cambiaron marcadamente entre diferentes sitios de muestreo en diferentes estaciones del año y sugirió que la mayoría de las cianobacterias no provenían a través del agua dulce. Se realizó un análisis de correspondencia canónica (CCA) para determinar la relación entre variables ambientales y estructuras de las comunidades bacterianas durante la estación seca. Los resultados sugirieron que las distintas comunidades de cianobacterias estaban significativamente influenciadas por el pH, salinidad, PO4-P y NO3-N en los sedimentos

Effect of temperature on the accumulation of marine biogenic gels in the surface microlayer near the outlet of nuclear power plants and adjacent areas in the Daya Bay, China

The surface microlayer (SML) in marine systems is often characterized by an enrichment of biogenic, gel-like particles, such as the polysaccharide-containing transparent exopolymer particles (TEP) and the protein-containing Coomassie stainable particles (CSP). This study investigated the distribution of TEP and CSP, in the SML and underlying water, as well as their bio-physical controlling factors in Daya Bay, an area impacted by warm discharge from two Nuclear power plants (Npp’s) and aquaculture during a research cruise in July 2014. The SML had higher proportions of cyanobacteria and of pico-size Chl a contrast to the underlayer water, particularly at the nearest outlet station characterized by higher temperature. Diatoms, dinoflagellates and chlorophyll a were depleted in the SML. Both CSP and TEP abundance and total area were enriched in the SML relative to the underlying water, with enrichment factors (EFs) of 1.5–3.4 for CSP numbers and 1.32–3.2 for TEP numbers. Although TEP and CSP showed highest concentration in the region where high productivity and high nutrient concertation were observed, EFs of gels and of dissolved organic carbon (DOC) and dissolved acidic polysaccharide (> 1 kDa), exhibited higher values near the outlet of the Npp’s than in the adjacent waters. The positive relation between EF’s of gels and temperature and the enrichment of cyanobacteria in the SML may be indicative of future conditions in a warmer ocean, suggesting potential effects on adjusting phytoplankton community, biogenic element cycling and air-sea exchange processe

Investigation of Spatial and Temporal Trends in Water Quality in Daya Bay, South China Sea

The objective is to identify the spatial and temporal variability of the hydrochemical quality of the water column in a subtropical coastal system, Daya Bay, China. Water samples were collected in four seasons at 12 monitoring sites. The Southeast Asian monsoons, northeasterly from October to the next April and southwesterly from May to September have also an important influence on water quality in Daya Bay. In the spatial pattern, two groups have been identified, with the help of multidimensional scaling analysis and cluster analysis. Cluster I consisted of the sites S3, S8, S10 and S11 in the west and north coastal parts of Daya Bay. Cluster I is mainly related to anthropogenic activities such as fish-farming. Cluster II consisted of the rest of the stations in the center, east and south parts of Daya Bay. Cluster II is mainly related to seawater exchange from South China Sea

A Sandwich Electrochemical Immunosensor Using Magnetic DNA Nanoprobes for Carcinoembryonic Antigen

A novel magnetic nanoparticle-based electrochemical immunoassay of carcinoembryonic antigen (CEA) was designed as a model using CEA antibody-functionalized magnetic beads [DNA/Fe3O4/ZrO2; Fe3O4 (core)/ZrO2 (shell) nano particles (ZMPs)] as immunosensing probes. To design the immunoassay, the CEA antibody and O-phenylenediamine (OPD) were initially immobilized on a chitosan/nano gold composite membrane on a glassy carbon electrode (GCE/CS-nano Au), which was used for CEA recognition. Then, horseradish peroxidase (HRP)-labeled anti-CEA antibodies (HRP-CEA Ab2) were bound to the surface of the synthesized magnetic ZMP nanoparticles as signal tag. Thus, the sandwich-type immune complex could be formed between secondary antibody (Ab2) modified DNA/ZMPs nanochains tagged by HRP and GCE/CS-nano Au. Unlike conventional nanoparticle-based electrochemical immunoassays, the recognition elements of this immunoassay included both electron mediators and enzyme labels, which obviously simplifies the electrochemical measurement process. The sandwich-type immunoassay format was used for online formation of the immunocomplex of CEA captured in the detection cell with an external magnet. The electrochemical signals derived from HRP during the reduction of H2O2 with OPD as electron mediator were measured. The method displayed a high sensitivity for CEA detection in the range of 0.008–200 ng/mL, with a detection limit of 5 pg/mL (estimated at a signal-to-noise ratio of 3). The precision, reproducibility, and stability of the immunoassay were good. The use of the assay was evaluated with clinical serum samples, and the results were in excellent accordance with those obtained using the standard enzyme-linked immunosorbent assay (ELISA) method. Thus, the magnetic nanoparticle-based assay format is a promising approach for clinical applications, and it could be further developed for the detection of other biomarkers in cancer diagnosis

Charmless Bs→PP,PV,VVB_s\to PP, PV, VV Decays Based on the six-quark Effective Hamiltonian with Strong Phase Effects II

We provide a systematic study of charmless $B_s \to PP, PV, VV$ decays ($P$ and $V$ denote pseudoscalar and vector mesons, respectively) based on an approximate six-quark operator effective Hamiltonian from QCD. The calculation of the relevant hard-scattering kernels is carried out, the resulting transition form factors are consistent with the results of QCD sum rule calculations. By taking into account important classes of power corrections involving "chirally-enhanced" terms and the vertex corrections as well as weak annihilation contributions with non-trivial strong phase, we present predictions for the branching ratios and CP asymmetries of $B_s$ decays into PP, PV and VV final states, and also for the corresponding polarization observables in VV final states. It is found that the weak annihilation contributions with non-trivial strong phase have remarkable effects on the observables in the color-suppressed and penguin-dominated decay modes. In addition, we discuss the SU(3) flavor symmetry and show that the symmetry relations are generally respected

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions