To achieve efficient genetic transformation in Dunaliella viridis,this study optimized key parameters including culture media,antibiotic selection markers,and electroporation conditions.The results demonstrated that adding 0.5%agar to solid media enabled normal algal growth.The D.viri-dis strain showed insensitivity to glufosinate-ammonium but was susceptible to chloramphenicol and chlorate,with growth significantly inhibited by 400 μg/mL chloramphenicol in liquid media and 300 μg/mL in solid plates.For electroporation,cells were harvested by centrifugation at 1 700g for 3 min to achieve optimal cell collection efficiency.Using the NR gene knockout vector pKS-diaCas9-sgRNA-NRKO de-livered via electroporation,nitrate reductase(NR)-deficient mutants were successfully selected on 100 mmol/L(30 g/L)sodium chlorate plate.Sequencing confirmed precise knockout at the second exon of the NR gene.This work establishes a foundational protocol for employing D.viridis as a bioreactor in genetic engineering applications.
Deep learning is rapidly becoming a key tool for wave forecasting.As a classic model in im-age processing,U-Net offers powerful capabilities for extracting deep information and thus holds great potential for intelligent,high-precision regional wave forecasting.This study focuses on winter waves in the North China Sea.We develop an intelligent regional wave-forecasting model based on the U-Net ar-chitecture,and this model can also be applied to ice-covered waters.Using this model,we design 54 ex-perimental schemes with three forecast lead times(6,12 and 24 h).These schemes combine three input variables:significant wave height,10 m wind speed,and sea ice concentration.After training and veri-fication,the U-Net model achieves mean absolute errors(MAE)and correlation coefficients(CC)of 0.08 m and 0.98(for a 6 h lead time),0.11 m and 0.96(for a 12 h lead time),and 0.17 m and 0.96(for a 24 h lead time).When sea ice is present,adding ice concentration information improves forecast accuracy.Taking Liaodong Bay as an example,the inclusion of ice information reduces the forecast mean significant wave height by up to 0.60 m,leading to a 67.7%increase in forecast accuracy.We also compare the U-Net model with a convolutional neural network(CNN).Over 90%of the North China Sea shows lower MAE values for U-Net,with average reductions of 0.05 m in the Bohai Sea and 0.07 m in the Yellow Sea.However,the U-Net model tends to underestimate extreme wave heights above 3 m.Therefore,future work should focus on improving forecasts for such extreme events.
To explore the applicability of multispectral remote sensing technology for seagrass bed in-vestigation in coastal waters,this study selected the seagrass beds along the coast of Houhai Village in Sanya as the research object.Aerial surveys were conducted using a multispectral unmanned aerial vehi-cle(UAV)to obtain high-resolution orthophotos and the normalized difference vegetation index(ND-VI).By combining these data with an unsupervised clustering classification method,seagrass distribu-tion identification and area quantification were achieved.The results showed that this method could ef-fectively identify patchily distributed seagrass.The total seagrass bed area in the study region was 14 633.1 m2,of which the area of concentrated seagrass distribution was 6 279.8 m2,and the actual seagrass-covered area was 4 501.5 m2.Based on these results,this study proposed a method for calcu-lating seagrass bed coverage as the ratio of the seagrass distribution area to the defined monitoring zone area.The impact of monitoring zone definition on coverage calculation results was systematically ana-lyzed,and the coverage of seagrass beds along the coast of Houhai Village in Sanya was determined to be 19.4%.Compared with traditional seagrass coverage estimation methods,multispectral UAV re-mote sensing technology significantly improved the monitoring coverage,efficiency,and objectivity of seagrass assessments,demonstrating strong applicability and reliability.This approach provides an ef-fective technical means for achieving routine and standardized ecological monitoring of seagrass beds.
To clarify the migration and transformation mechanisms of dissolved organic matter(DOM)in estuaries under the influence of multiple factors,a comprehensive survey was conducted in the Changjiang Estuary and its adjacent sea area in summer 2021.By integrating multiple parameters inclu-ding the absorption and excitation-emission matrix(EEM)fluorescence spectroscopy of Colored DOM(CDOM),dissolved organic carbon(DOC)concentration,and the composition and enantiomeric ratios of total hydrolyzable amino acids(THAA),the distribution characteristics,sources,transformation,and key controlling processes of DOM were systematically investigated.The results showed that both DOC concentration(1.21～2.11 mg/L)and the CDOM absorption coefficient a(355)(0.48～2.14 m-1)exhibited a significant seaward decreasing gradient and were significantly negatively correlated with salinity(r=-0.89,p<0.01),confirming that physical mixing is the primary factor controlling the macroscopic distribution of DOM.Parallel factor analysis identified two humic-like and two protein-like components.Their relative composition shifted from a dominance of terrestrial humic-like compo-nents(accounting for 65%～75%)in the nearshore to a dominance of marine-sourced protein-like com-ponents(accounting for 45%～60%)in the offshore waters.The spectral slope ratio showed a positive correlation with salinity(r=0.83,p<0.01),indicating a decrease in molecular weight and aromaticity of DOM during its seaward transport,attributable to photochemical degradation.Molecular-level amino acid information further elucidated the spatial differences in DOM sources and degradation status.The contribution of THAA to DOC(THAA-C%)increased from 2.71%in the nearshore region to 5.09%in the offshore region.Influenced by the Changjiang River input,the nearshore region was dominated by terrestrial,highly degraded DOM,characterized by a lower THAA-C%and predominantly negative DI values.In contrast,the offshore region exhibited enhanced phytoplankton production and a higher pro-portion of fresh DOM,leading to an increased THAA-C%and a shift of DI values toward predominant-ly positive values.The increase in the D/L ratio of amino acids in bottom waters directly demonstrated the ongoing microbial degradation.Redundancy analysis further revealed that the composition and trans-formation of DOM are synergistically regulated by physical mixing,photodegradation,and microbial ac-tivity,and a significant coupling effect between photodegradation and microbial degradation was identi-fied.These findings deepen the understanding of DOM biogeochemical mechanisms in estuarine areas from a multi-process coupling perspective and provide new basis for quantifying the fate of terrestrial carbon in coastal ecosystems.
This article investigates the regular points of the quasi-periodic discrete Schrödinger operator Hωun=un-1+un+1+vTnω un which isgenerated by the irrational rotation on the circle.By employing the uniformly hyperbolic property of the corresponding Schrödinger cocycle,we construct the phase-angle sequence and analyze the asymptotic behavior of the Prüfer angle of the operator characteristic equation.Based on the phase-angle sequence and the definition of the rotation number,a new method for calculating the rotation number is given.
