Table_1_Toxic Effects of Copper Nanoparticles on Paramecium bursaria–Chlorella Symbiotic System.DOCX

Although many reports have demonstrated that nanoparticles can have a negative effect on aquatic organisms, the toxic effects on symbiotic organisms remain poorly understood. The present study conducts ultrastructure, enzyme activity, and transcriptomics to assess the toxic effects to the Paramecium bursaria–Chlorella symbiotic system from exposure to copper nanoparticles (CuNPs) for 24 h. We found that in both the host and symbiotic algae, CuNP exposure induced high reactive oxygen species level, which leads to oxidative damage and energy metabolism disorder. Moreover, transmission electron micrographs (TEMs) showed that the symbiotic algae in the cytoplasm of P. bursaria were enveloped in the digestive vacuole and digested, and the level of acid phosphatase activity increased significantly within 24 h, which indicated that the stability of the symbiotic system was affected after CuNP exposure. We speculated that the increased energy demand in the host and symbiotic algae resulted from oxidative stress, precipitating the decrease of the photosynthetic products provided to the host, the digestion of the symbiont, and the destruction of the stable symbiotic relationship. The study provides the first insight into the mechanisms of nanoparticles’ toxicity to the symbiotic relationship in the ecosystem, which may help to understand the environmental effects and toxicological mechanisms of nanoparticles.</p

MOESM2 of Fed-batch enzymatic hydrolysis of alkaline organosolv-pretreated corn stover facilitating high concentrations and yields of fermentable sugars for microbial lipid production

Additional file 2. Sugar evolution profiles of fed-batch enzymatic hydrolysis with different feeding times

MOESM3 of Fed-batch enzymatic hydrolysis of alkaline organosolv-pretreated corn stover facilitating high concentrations and yields of fermentable sugars for microbial lipid production

Additional file 3. Effects of initial sugar concentrations of the hydrolysate on the cell growth and lipid production by C. oleaginosum

MOESM1 of Fed-batch enzymatic hydrolysis of alkaline organosolv-pretreated corn stover facilitating high concentrations and yields of fermentable sugars for microbial lipid production

Additional file 1. Time course of sugar evolution during the batch enzymatic hydrolysis at solids loadings ranging from 8 to 14% (w/v)

DataSheet1_Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis.ZIP

Dynamic assessment of cerebral blood flow (CBF) is crucial for guiding personalized management and treatment strategies, and improving the prognosis of stroke. However, a safe, reliable, and effective method for dynamic CBF evaluation is currently lacking in clinical practice. In this study, we developed a CBF monitoring system utilizing electromagnetic coupling sensing (ECS). This system detects variations in brain conductivity and dielectric constant by identifying the resonant frequency (RF) in an equivalent circuit containing both magnetic induction and electrical coupling. We evaluated the performance of the system using a self-made physical model of blood vessel pulsation to test pulsatile CBF. Additionally, we recruited 29 healthy volunteers to monitor cerebral oxygen (CO), cerebral blood flow velocity (CBFV) data and RF data before and after caffeine consumption. We analyzed RF and CBFV trends during immediate responses to abnormal intracranial blood supply, induced by changes in vascular stiffness, and compared them with CO data. Furthermore, we explored a method of dynamically assessing the overall level of CBF by leveraging image feature analysis. Experimental testing substantiates that this system provides a detection range and depth enhanced by three to four times compared to conventional electromagnetic detection techniques, thereby comprehensively covering the principal intracranial blood supply areas. And the system effectively captures CBF responses under different intravascular pressure stimulations. In healthy volunteers, as cerebral vascular stiffness increases and CO decreases due to caffeine intake, the RF pulsation amplitude diminishes progressively. Upon extraction and selection of image features, widely used machine learning algorithms exhibit commendable performance in classifying overall CBF levels. These results highlight that our proposed methodology, predicated on ECS and image feature analysis, enables the capture of immediate responses of abnormal intracranial blood supply triggered by alterations in vascular stiffness. Moreover, it provides an accurate diagnosis of the overall CBF level under varying physiological conditions.</p