Effect of extracellular polymeric substances on Dolichospermum aggregation during temperature rise

Dolichospermum,a typical model filamentous of cyanobacteria,has the potential to cause severely bloom.Extracellular polymeric substances(EPSs)are considered to influence the aggregation of the algae,and temperature is a significant factor affecting EPSs secretion.However,the mechanism of how EPSs affects the aggregation of Dolichospermum is still unclear because the structure and composition of EPSs are complex.In this study,the effects of EPSs on the aggregation of Dolichospermum during the rise of temperature(7-37℃)were determined.The results showed that the concentration of extracellular polysaccharides and proteins changed significantly with increasing temperature(P<0.01).Firstly,during the increasing temperature,the polysaccharide content of EPSs increased from 20.34 to 54.64 mg/L,and the polysaccharides in the soluble EPS(S-EPS)layer changed significantly.The protein content reached maximum value at 21℃(14.52 mg/L)and varied significantly in S-EPS and loosely bound EPS(LB-EPS).In the EPSs matrix,humus substances and protein were main components of S-EPS and LB-EPS,and protein was the main component of tightly bound EPS(TB-EPS).Secondly,the cell density of Dolichospermum increased during the temperature rise while the aggregation ratio decreased.Moreover,zeta potential and surface thermodynamic analysis of Dolichospermum revealed that the interfacial free energy and electrostatic repulsion increased gradually with increasing temperature,which further reduced the aggregation of Dolichospermum.Finally,principal component analysis(PCA)analysis showed the aggregation of Dolichospermum was directly related to the changes of protein in EPSs(especially S-EPS and LB-EPS)and zeta potential,and polysaccharides in EPSs inhibited the aggregation of Dolichospermum.Based on these results,it was illustrated that the composition and concentration of EPSs affected the cell surface properties of Dolichospermum with the change of temperature and thus affected the aggregation of Dolichospermum.

Anisotropic Total Variation Regularization Based NAS-RIF Blind Restoration Method for OCT Image

Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.

A CNN-LSTM-PSO tool wear prediction method based on multi-channel feature fusion

In order to achieve predictive maintenance of CNC machining tools and to be able to change tools intelligently before tool wear is at a critical threshold,a CNN-LSTM tool wear prediction model based on particle swarm algorithm(PSO)optimization with multi-channel feature fusion is proposed.Firstly,the raw signals of seven channels of the machining process are collected using sensor technology and processed for noise reduction;secondly,the time-domain,frequency-domain and time-frequency-domain features of each channel signal are extracted,and a sample data set of spatio-temporal correlation of traffic flow is constructed by dimensionality reduction processing and information fusion of the above features;finally,the data set is input to the CNN-LSTM-PSO model for training and testing.The results show that the CNN-LSTM-PSO model can effectively predict tool wear with an average absolute error MAE value of 0.5848,a root mean square error RMSE value of 0.7281,and a coefficient of determination R2 value of 0.9964;and compared with the BP model,CNN model,LSTM model and CNN-LSTM model,its tool wear prediction accuracy improved by 7.56%,2.60%,2.98%,and 1.63%,respectively.