Phaeocystis globosa is an important harmful algal species that is globally distributed. Previous studies have indicated that P. globosa preferentially uptake nitrate, but the underlying mechanism is still unclear. To further verify this preference and reveal the underlying mechanism, the assimilation rates of nitrate and ammonium by P. globosa at different concentrations was quantitatively studied by using a nitrogen stable isotope (¹⁵N) tracer technique, and the regulatory mechanism was determined from the physiological and biochemical responses. The findings revealed that the preferential assimilation of nitrate by P. globosa was influenced by the ambient ammonium concentration. When the ambient concentration of ammonium was less than approximately 3.5 μmol·L⁻¹, the assimilation rates of nitrate form P. globosa were as high as 1.05 × 10⁻⁵ μmol·L⁻¹·h⁻¹·cell⁻¹. Even though the nitrate assimilation in P. globosa was inhibited at ammonium concentrations greater than this threshold, nitrate assimilation could not be completely suppressed. The activity of NR and the expression of related genes in P. globosa were also affected by ammonium. In addition, ¹⁵N signals originally labeled nitrate accumulated in ammonium. This indicated that P. globosa was likely to reduce excess nitrate to ammonium and subsequently release it into the substrate, which might be an important energy dissipation mechanism for P. globosa. These results expand the classical understanding of the utilization of nitrogen nutrients by marine phytoplankton, and offer valuable resources for comprehending the mechanism of harmful algal blooms.

Generative artificial intelligence (GenAI) has been increasingly integrated in academic publishing process. This study aims to access the performance of GenAI in peer review using multi-disciplinary manuscript samples. Five GenAI models underwent peer review tests, systematically evaluated for completeness, accuracy, and rationality of responses using combined manual scoring and statistical analysis. Findings indicated high accuracy in summarizing manuscript content and identifying suitable journals. However, GenAI models exhibited significant limitations in identifying scientific errors and evaluating paper innovation. The results demonstrate clear competency boundaries for GenAI within the peer review, particularly concerning tasks requiring nuanced professional judgment. This study clarifies the potential application scope of GenAI in peer review and provides a basis for its responsible future implementation.

Generative artificial intelligence (GenAI) has been increasingly integrated in academic publishing process. This study aims to access the performance of GenAI in peer review using multi-disciplinary manuscript samples. Five GenAI models underwent peer review tests, systematically evaluated for completeness, accuracy, and rationality of responses using combined manual scoring and statistical analysis. Findings indicated high accuracy in summarizing manuscript content and identifying suitable journals. However, GenAI models exhibited significant limitations in identifying scientific errors and evaluating paper innovation. The results demonstrate clear competency boundaries for GenAI within the peer review, particularly concerning tasks requiring nuanced professional judgment. This study clarifies the potential application scope of GenAI in peer review and provides a basis for its responsible future implementation.