Toxicity interaction of polystyrene nanoplastics with sulfamethoxazole on the microalgae Chlamydomonas reinhardtii: A closer look at effect of light availability.

The coexistence of nanoplastics and antibiotics in the aquatic environment has raised a complicated risk for ecosystems and human health. How the environmental factors e.g., light, regulate the interaction between nanoplastics and antibiotics and the resulting combined toxicity is poorly understood. Here, we investigated the individual and combined toxicity of polystyrene nanoplastics (nPS, 100 mg L1) and sulfamethoxazole (SMX, 2.5 and 10 mg L-1) toward the microalgae Chlamydomonas reinhardtii under low (LL, 16 µmol m-2·s-1), normal (NL, 40 µmol m-2·s-1), and high light (HL, 150 µmol m-2·s-1) in terms of cellular responses. Results indicated that the joint toxicity of nPS and SMX commonly exhibited a strong antagonistic/mitigative effect under LL/NL at 24 h, and under NL at 72 h. nPS could adsorb more SMX under LL/NL at 24 h (1.90/1.33 mg g-1) and under NL at 72 h (1.01 mg g-1), thereby alleviating SMX toxicity to C. reinhardtii. However, the self-toxicity of nPS had a negative influence on the degree of antagonism between nPS and SMX. The experimental results coupled with computational chemistry further revealed that the adsorption capacity of SMX on nPS was stimulated by low pH under LL/NL at 24 h (∼7.5), while by less co-existing saline ions (0.83 ppt) and algae-derived dissolved organic matter (9.04 mg L-1) under NL at 72 h. nPS toxicity that was responsible for the toxic action modes was mainly attributed to the shading effect induced by hetero-aggregation and hindrance of light transmittance (>60%), as well as being regulated by additives leaching (0.49-1.07 mg L-1) and oxidative stress. Overall, these findings provided a critical basis for the risk assessment and management of multiple pollutants in the complex natural environment.

Shear wave trajectory detection in ultra-fast M-mode images for liver fibrosis assessment: A deep learning-based line detection approach.

Stiffness measurement using shear wave propagation velocity has been the most common non-invasive method for liver fibrosis assessment. The velocity is captured through a trace recorded by transient ultrasonographic elastography, with the slope indicating the velocity of the wave. However, due to various factors such as noise and shear wave attenuation, detecting shear wave trajectory on wave propagation maps is a challenging task. In this work, we made the first attempt to use deep learning methods for shear wave trajectory detection on wave propagation maps. Specifically, we adopted five deep learning models in this task and evaluated them by using a well-acknowledged metric based on EA-Angular-Score (EAA) and task-specific metric based on Young s-Score (Ys) in the line-detection field. Furthermore, we proposed an end-to-end framework based on a Transformer and Hough transform, named Transformer-enhanced Hough Transform (TEHT). It took a wave propagation map as input image and directly output the slope of the shear wave trajectory. The framework extracts multi-scale local features from wave propagation maps, employs a deformable attention mechanism for feature fusion, identifies the target line using the Hough transform's voting mechanism, and calculates the contribution of each scale through channel attention. Wave propagation maps from 68 patients were utilized in this study, with manual annotation performed by a rater who was trained as a radiologist, serving as the reference value. The evaluation revealed that the SLNet model exhibited F-measure of EA and Ys values as 40.33 % and 40.72 %, respectively, while the TEHT model showed F-measure of EA and Ys values as 80.96 % and 98.00 %, respectively. TEHT yielded significantly better performance than other deep learning models. Moreover, TEHT demonstrated strong concordance with the gold standard, yielding R2 values of 0.967 and 0.968 for velocity and liver stiffness, respectively. The present study therefore suggests the application of the TEHT model for assessing liver fibrosis owing to its superiority among the five deep learning models.

A Novel Approach for Measurement of Free-Form Optical Elements with Digital Holographic Microscopy.

Free-form optical elements face significant challenges in high-precision measurement due to their high complexity and non-rotational symmetry. Digital holographic microscopy (DHM), as one of the methods for the measurement of free-form optical elements, has promising applications due to its ultra-high precision and non-destructive and fast characteristics. Therefore, we have designed a novel measurement method that combines transmission DHM and reflection DHM to obtain thickness information and surface information of elements to deduce the 3D structure. With this method, we completed the measurement of a free-form optical element. The DHM system we built has recorded holograms under 4× and 20× objectives and successfully recovered the 3D surface shape of the element. The measurements are consistent with the designed and manufactured parameters, demonstrating the unique advantages of DHM for measuring special types of optical elements.