Expression and significance of cathepsin C and cathepsin D during pregnancy and Preeclampsia.

BACKGROUND: Cathepsin C (Cat C) is involved in the inflammatory-immune system and can be degraded by cathepsin D (Cat D). Preeclampsia (PE) and the inflammation-immunity relationship is currently a hot research topic, but there are still few studies. The aim was to investigate the expression and significance of Cat C and D in the serum of nonpregnant women, patients in various stages of pregnancy and patients with PE, and in the placenta of patients with normal pregnancy and PE. METHODS: Sixty young healthy nonpregnant women were selected: 180 normal pregnant women, including 60 each in the first, second, and third trimesters, and 100 women with PE, including 39 women with severe preeclampsia. The levels of Cat C and D in serum were detected by enzyme-linked immunosorbent assay (ELISA), and the expression levels of Cat C and D in placentas were detected by immunohistochemistry (IHC). RESULTS: The serum of Cat C in the first trimester was significantly lower than that in the nonpregnant group (P < 0.001), whereas Cat D was significantly higher than that in the nonpregnant group (P < 0.01). The levels of Cat C and D in the second trimester and third trimester were significantly higher than those in the first trimester (P < 0.05), but there was no significant difference in Cat C and D between the second trimester and third trimester. The levels of Cat C in the serum and placentas of patients with PE were significantly higher than those in the third trimester (P < 0.001) and positively correlated with the severity of PE (P < 0.001), whereas the levels of Cat D in the serum and placentas of patients with PE were significantly lower than those in the third trimester (P < 0.001) and negatively correlated with the severity of PE (P < 0.001). Age, primigravida proportion, and body mass index were significantly higher in the PE group than in the control group (P < 0.05), which were high-risk factors for PE. CONCLUSIONS: Cat C and D are associated with the maintenance of normal pregnancy. In patients with preeclampsia, a significant increase in Cat C and a significant decrease in Cat D levels may lead to the occurrence and development of preeclampsia.

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients.

Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingredients on the splitting-tensile strength. The model's performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Then, the model's performance was validated using k-fold cross-validation. The random forest model, with an R2 of 0.96, outperformed the AdaBoost models. The random forest models with greater R2 and lower error (RMSE = 0.49) had superior performance. It was revealed from the SHAP analysis that the cement content had the highest positive influence on the splitting-tensile strength of the recycled aggregate concrete and the primary contact of cement is with water. The feature interaction plot shows that high water content has a negative impact on the recycled aggregate concrete (RAC) splitting-tensile strength, but the increased cement content had a beneficial effect.

Single-shot phase retrieval based on beam splitting.

A kind of beam-splitting-based single-shot phase retrieval is proposed, where the transmitted field of a sample to be observed is diffracted into many replicas by a Dammann grating and then incident on a weakly scattering phase plate with a known structure, and the exiting beams propagate roughly along their original directions and form a diffraction pattern array on the detector. All sub-diffraction patterns isolated with each other were recorded with a single measurement, and the complex amplitude of the radiation incident on the grating and accordingly transmitted from the sample can be iteratively reconstructed from the recorded diffraction pattern array. Since the weakly scattering plate is accurately measured in advance, fast convergence and high accuracy can be achieved with a small number of sub-diffraction patterns. The feasibility of this proposed method is verified with experiments using visible light.

Ptycholographic iterative engine with self-positioned scanning illumination.

A special optical alignment is adopted and corresponding reconstruction algorithm is developed to reduce the reconstruction error induced by the hysteresis or backlash error of the translation stage in Ptychographical Iterative Engine (PIE) imaging with weak scattering specimen. In this suggested method, the positions of the scanning probe are determined directly from the recorded diffraction patterns rather than from the readout of the stage meter. This method not only remarkably improves the reconstruction quality, but also completely lowers the dependency of PIE on the device accuracy and accordingly enhances its feasibility for many applications with weak scattering specimen.