Melanin is one of the essential compounds in the pigments of molluscan shells. However, the effects of melanin on color variations in molluscs are largely unknown. Our previous study suggests that Yesso scallop Patinopecten yessoensis might contain melanin pigment in the dark brown shell. We therefore isolated melanin from the pigmented shells using hydrochloric acid method, and characterized the types of melanin pigments by spectrophotometry. The purified melanin, which was verified by spectrophotometry scanning and HPLC analysis, showed the typical characteristics of melanin absorption spectra and HPLC chromatograms. The contents of pheomelanin and eumelanin in pigmented shells, which were determined by the linear standard curve of melanin at 405 nm and 350 nm absorbance, were 48.23 ± 1.350 and 157.65 ± 5.905 mg, respectively. The present results indicate that the brown-pigmented shells of scallops comprise approximately 76.6% of eumelanin and 23.4% of pheomelanin, which supports the presence of eumelanin-rich pigment in scallop shells. Therefore, the combination of hydrochloric acid extraction and spectrophotometric quantification is a rapid and efficient method to isolate and quantify melanin in shells. This will facilitate the melanin studies related to shell color polymorphism and the selective breeding of bivalves with different shell colors. 相似文献
Dissolved organic matter(DOM) plays a vital role in promoting carbon and nutrient cycling.It is a food source for organisms and controls the migration and transformation of trace metals and other contaminants in aquatic systems. The contributions of aquatic DOM to the environment and ecology of a system are closely related to its abundance and chemical structure. In this study, the chemical composition and binding properties of DOM in a hypersaline lake watershed were investigated for the first ... 相似文献
Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.
This paper is aimed at creating an empirical model for assessing failure potential of highway slopes, with a special attention to the failure characteristics of the highway slopes in the Alishan, Taiwan area prior to, and post, the 1999 Chi-Chi, Taiwan earthquake. The basis of the study is a large database of 955 slope records from four highways in the Alishan area. Artificial neural network (ANN) is utilized to “learn” from this database. The developed ANN model is then used to study the effect of the Chi-Chi earthquake on the slope failure characteristics in the Alishan area. Significant changes in the degrees of influence of several factors (variables) are found and possible reasons for such changes are discussed. The novelty of this paper lies in the fact that the developed ANN models are used as a tool to investigate the slope failure characteristics before and after the Chi-Chi earthquake. 相似文献
