Characterizing the vertical distribution of chlorophyll a in the water column in each oceanic region is crucial for accurate assessment of depth-integrated phytoplankton biomass. In this study, the characteristics of the subsurface chlorophyll maximum (SCM) in the South China Sea (SCS) during the boreal summer were investigated by using bio-optical and hydrological data collected during four cruises from 2008 to 2015. During the boreal summer, a well-developed SCM layer was found to be a prominent feature in the SCS, with the thickness, depth, and magnitude of the SCM exhibiting large spatial variability. The vertical position of the SCM varied between 11 and 99 m, with an average value of 53 m. Light attenuation played a fundamental role in determining the depth (Z_SCM) and magnitude of the SCM (Chl_max), as reflected by their relationships with the euphotic zone depth (Z_eu). However, because significant positive correlations were found between Z_SCM and the depth of potential density at 23 kg m^-3 (Z_σ=23), physical processes were inferred to be more important in modulating the fluctuation of Z_SCM, especially in open-ocean areas. Anticyclonic eddies, which act to deepen the nutricline by means of isopyncnal displacement, may play a role in aggravating nutrient limitation in the SCM layer, which leads to deepening of Z_SCM, weakening of Chl_max, and a fall in the total integrated chlorophyll a within the euphotic layer (Chl_intZeu). Knowledge of ocean physical conditions and the variability of Z_SCM should be taken into consideration to improve the accuracy of Chl_intZeu estimates based on the surface chlorophyll a concentration (Chl_surf), especially when Chl_surf is very low. Our assessment of SCM parameters provides the basis for a better understanding and quantification of their role in primary production estimation within the SCS.

Malignant bone tumor osteosarcoma (OS) displays high metastasis incidence and poor prognosis. Its stem cell properties could serve to explain tumor recurrence and resistance to conventional treatments. In this study, we identified UEV1A as a novel suppressor of OS. Elevated UEV1A diminishes stem cell properties of OS cells and drives them to terminal differentiation. Importantly, UEV1A-overexpressed OS cells delay proliferation and are more sensitive to chemotherapeutic agents than control cells. Uev1A appears to be involved in the BMP signaling pathway in which it collaborates with a ubiquitin E3 ligase Smurf1 to promote Smad1 degradation in a Ubc13-independent manner. Indeed, Smad1 is identified as a dominant downstream effector of Uev1A, which unravels the mechanism underlying Uev1A-orchestrated tumor suppression in OS. The above findings identify UEV1A as a potential OS tumor suppression gene, and shed lights to future OS diagnosis and treatment.

The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (a_t-w(λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla from a_t-w(λ), and then extracting PSC from them using the support vector machine (SVM). In situ bio-optical data collected in the South China Sea from 2006 to 2013 were used to train the SVM. The proposed PSC model was subsequently validated using an independent PSC dataset from the Northeast South China Sea Cruise in 2015. The results indicate that the PSC model performed better than the three components model, with a value of r² between 0.35 and 0.66, and the absolute percentage difference between 56% and 181%. On the whole, our PSC model shows a remarkable utility in terms of inferring vertical PSCs from the South China Sea.

An empirical algorithm is proposed to estimate suspended particulate matter (SPM) ranging from 0.675 to 25.7 mg L⁻¹ in the turbid Pearl River estuary (PRE). Comparisons between model predicted and in situ measured SPM resulted in R²s of 0.97 and 0.88 and mean absolute percentage errors (MAPEs) of 23.96% and 29.69% by using the calibration and validation data sets, respectively. The developed algorithm demonstrated the highest accuracy when compared with existing ones for turbid coastal waters. The diurnal dynamics of SPM was revealed by applying the proposed algorithm to reflectance data collected by a moored buoy in the PRE. The established algorithm was implemented to Hyperspectral Imager for the Coastal Ocean (HICO) data and the distribution pattern of SPM in the PRE was elucidated. Validation of HICO-derived reflectance data by using concurrent MODIS/Aqua data as a benchmark indicated their reliability. Factors influencing variability of SPM in the PRE were analyzed, which implicated the combined effects of wind, tide, rainfall, and circulation as the cause.

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.