Heterocapsa bohaiensis is a newly identified dinoflagellate species that causes harmful blooms in coastal areas in China, Malaysian, and New Caledonian. These blooms have led to substantial economic losses for local aquaculture. Previous studies have mainly focused on understanding the toxicity of H. bohaiensis. However, the causes of H. bohaiensis blooms remain unknown. In this study, we aimed to ascertain nitrogen (N) and phosphorus (P) requirements for the growth and reproduction of H. bohaiensis. Additionally, we sought to understand the functional mechanisms by comparing the transcriptomes of H. bohaiensis under nutrient-limited conditions and control conditions. The results revealed a wide range of acceptable N:P ratios for H. bohainensis, attributed to a mechanism involving nutrient storage, which allowed H. bohainensis to sustain its growth even when either nitrate or phosphate was depleted. Higher N:P ratios (> 27.5) were more conducive to the growth of H. bohainensis than f/2 medium or low ratios, which is related to the N:P ratios absorbed by H. bohainensis. The toxicity of H. bohainensis was significantly enhanced in N-limited or P-limited states. These findings underscore the significance of the physiological metabolism of H. bohainensis in adapting to environmental stresses induced by human activities and establishing the dominance of blooms.
Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.