Algal blooms pose serious threats to water environments and the balance of aquatic ecosystems. Moreover, environmental factors may be the primary driver of bloom outbreaks. Studying the characteristics and driving factors of the evolution of cyanobacterial blooms can provide a scientific basis for the restoration of lake water environments and aquatic ecosystems. Based on the Landsat extended time series and practical ecological investigation, this study determined the bloom area and dominant species of cyanobacterial blooms in Hulun Lake, and analyzed their evolution characteristics and primary influencing factors. The results show that the area of bloom outbreak tended to decrease from 2018 to 2020, with the peak area remaining below 150 km²; in contrast, the bloom area showed an increasing trend in 2021–2022, with a maximum of 1970.55 km² in June 2022. Pearson correlation analysis of bloom area and driving factors showed that the wind speed was the main influencing factor of bloom outbreaks. In 2022, there were five dominant species of cyanobacteria in summer and three in autumn. Redundancy analysis of the dominant species of cyanobacteria and water quality indicators showed that COD, DO, TP, WT, pH, and TN were the main influencing factors of the dominant species of cyanobacteria in summer, and COD, DO, TP, and WT were the main influencing factors in autumn. In general, cyanobacteria in Hulun Lake are sensitive to changes in wind speed and nutrient concentration. An important approach towards effectively reducing the intensity of cyanobacterial blooms in Hulun Lake and coping with the ecological risks associated with the changing environment would be to reduce external loads and regulate the ecosystem structure of the lake. In the future, while studying the law of cyanobacterial bloom outbreaks, attention should be paid to the impact of the cyanobacterial physiological status on bloom outbreaks.

Climate change and eutrophication have significant impacts on aquatic ecosystems, with phytoplankton playing a critical role as primary producers. This study investigates the relationships between phytoplankton communities and environmental and meteorological factors in Wuliangsuhai Lake, a representative ecosystem in an arid and cold region of China. Using data from 25 sampling sites (April–September 2023), a generalized additive model (GAM) was employed to analyze water quality and laboratory measurements. The results showed a bimodal distribution of phytoplankton density, with peaks in July (5.33 × 10⁶ cells/L) and August (14.90 × 10⁶ cells/L). Green algae dominated in spring, while cyanobacteria became dominant in summer. GAM analysis revealed that temperature (TEMP) was the primary driver, explaining 20.7% of the deviance. When TEMP was examined together with other factors, the explanatory ability of the model was significantly enhanced, and finally, the model explained 57.10% of the deviance. GAM analysis also revealed that different algae species responded differently to environmental factors, with the cyanophyta-dominant species Pseudoalgae being more sensitive to TEMP and pH, whereas the chlorophyta-dominant species Chlorella was more affected by wind speed and salinity. Cyanobacteria dominance, exacerbated by warming, increases the risks of harmful algae blooms and ecosystem instability, highlighting the need for mitigation strategies in vulnerable freshwater ecosystems.

Bacteria play a critical role in the material and energy-cycling processes of lake ecosystems. To understand the characteristics of the bacterial community in Wuliangsuhai Lake in spring, we explored the influence of environmental factors on the community structure of particle-attached bacteria (PA) and free-living bacteria (FL) in the water column of Wuliangsuhai Lake. In this study, we analyzed the bacterial community characteristics of 10 sampling sites in Wuliangsuhai Lake in April 2019 based on the high-throughput sequencing of 16S rRNA genes. Redundancy analysis (RDA) was used to analyze the influence of environmental factors on bacterial communities in lake water. The results showed the following: (1) The relative abundance of bacteria in Wuliangsuhai Lake did not significantly differ among the 10 sampling sites, and the dominant bacterial phyla were Actinobacteria, Proteobacteria, and Bacteroidetes. In addition, the community diversity of particle-attached (PA) was higher than that of free-living (FL). (2) The relative abundance of Bacteroidetes in PA (28.83%~54.67%) was significantly higher than that of FL (10.56%~28.44%), the relative abundance of Actinobacteria in the number of PA (20.02%~61.61%) was lower than that of FL (8.18%~16.71%), and the relative abundance of Verrucomicrobia in the PA (0.55%~13.11%) was higher than that of FL (0.05%~6.31%). (3) The redundancy analysis (RDA) showed that transparency, total nitrogen, total phosphorus, and ${\mathrm{NH}}_4^{+}$-N were the main factors influencing the dominant bacterial communities in Wuliangsuhai Lake. This study provides the basis for further research on bacterial communities in freshwater lakes and may help local governments in the management of the water resources of Wuliangsuhai Lake.

Purpose: The aims of this study were to identify the prognosis-related risk factors for HCC patients after surgery and to develop a predictive model by analysing the medical records of 152 HCC patients in our hospital. Patients and Methods: Univariate Cox regression analysis was applied to identify potential risk factors for HCC patients after surgery and to determine independent prognosis-related risk factors by multivariate analysis. Subsequently, a nomogram model was developed based on all independent factors and was validated by a validation set. Calibration and receiver operating characteristic curves were employed to evaluate the accuracy of the model. Finally, decision curve analyses were used to assess its clinical utility. Results: The univariate Cox regression analysis indicated that the patient’s age, sex, grade, different AJCC TNM stages, vascular invasion, lymphatic infiltration, and tumour size were potential prognostic-related risk factors for HCC patients (p < 0.2), and the findings of multivariate analysis revealed that grade, different AJCC TNM stages, vascular invasion, and lymphatic infiltration were independent prognostic-related risk factors for HCC patients (p < 0.05). Subsequently, we constructed a prognosis-related prediction model based on all independent prognostic predictors and validated it with internal and external validation sets. The validation results indicated that the prediction model showed good accuracy (AUC = 0.81, 0.728) and consistency. More importantly, decision curve analysis illustrated that the nomogram model is a practical tool for predicting prognosis. Conclusion: This study found that grade, different AJCC TNM stages, vascular invasion, and lymphatic infiltration were independent prognosis-related predictors for HCC patients after surgery, and a nomogram model built on these predictors exhibited great accuracy and clinical usefulness.

The primary productivity of phytoplankton (PP_eu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PP_eu using remote sensing techniques is constrained. To address this limitation, this study developed an estimation model for ice-covered PP_eu by incorporating optical parameters such as the ice surface refractive index and the extinction coefficient of the ice layer into the vertical generalized production model (VGPM). This approach overcomes the challenges associated with remote sensing-based estimation of PP_eu during ice-covered periods. The results indicate that the annual carbon sequestration of the WLSHL is 1.72 × 10⁴ t C, with an average annual PP_eu of 316.96 mg C·m⁻²·d⁻¹. In addition to the indicators that are directly involved in the estimation of PP_eu, the environmental factors that affect PP_eu include water temperature (WT), ice thickness (IT), snow, water depth (D), total dissolved solids (TDSs), salinity (S), ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and oxidation–reduction potential (ORP). The PP_eu in the ice period is found to be only 17% lower than that in the ice-free period. However, the PP_eu during the ice period is considerably higher than that during the ice + snow period. The findings indicate that the impact of freezing on PP_eu during the winter is relatively limited, whereas the influence of snowfall is more pronounced. In order to mitigate the elevated PP_eu and the occurrence of algal blooms during the summer, the intensity of underwater radiation can be regulated on a periodic basis. To optimize the function of the carbon sink in winter lakes, the PP_eu can be enhanced through initiatives such as water replenishment prior to freezing and snow removal following freezing.