Characterization of Cassava Starch-Stearic Acid Complex Nanoparticles and Stability of Pickering Emulsions Stabilized by It

In order to study the feasibility of applying cassava starch-fatty acid complexes as a Pickering emulsion stabilizer, complex nanoparticles with complexing index (CPI) of 2.74%, 9.17% and 27.66% were prepared by mixing cassava starch paste containing 78.65% amylopectin at 95 ℃ and stearic acid followed by alcohol precipitation. The three complexes had an irregular spherical-like shape under field emission scanning electron microscopy (FESEM), and their average particle sizes, determined by a laser particle size analyzer, were 315.35, 348.19 and 427.60 nm, respectively. The X-ray diffraction pattern of each of the complexes showed two peaks at 13° and 21°, which were characteristics of the V type crystal structure, and the crystal content increase with increasing CPI. Their deconvoluted infrared spectra exhibited changes in short-range ordering at 1 047, 1 022 and 995 cm-1. The contact angle of the particles with the highest CPI was 60.30°. The three complex nanoparticles stabilized Pickering emulsions for more than seven days compared to less than two days with starch nanoparticles. The complex nanoparticles with CPI of 27.66% stabilized emulsions best. The addition of the complex nanoparticles with CPI of 27.66% at levels above 0.1 g/100 mL resulted in the formation of an emulsion with an oil-to-water ratio of 1:9 (V/V). The emulsion with this nanoparticle at 7 g/100 mL exhibited an improved stability for 60 days without creaming or phase separation. Moreover, no significant changes in the droplet size distribution were observed. The emulsion was stable at pH 5.6-9.0 and not affected by NaCl concentration in the range of 0.01-0.1 mol/L. The emulsion maintained its morphology well after being heated to 80 ℃. These results suggest that the complex nanoparticles are a potential Pickering emulsion stabilizer

A novel reconstruction algorithm based on density clustering for cosmic-ray muon scattering inspection

As a relatively new radiation imaging method, the cosmic-ray muon scattering imaging technology can be used to prevent nuclear smuggling and is of considerable significance to nuclear safety. Proposed in this paper is a new reconstruction algorithm based on density clustering, aiming to improve inspection quality with better performance. Firstly, this new algorithm is introduced in detail. Then in order to eliminate the inequity of the density threshold caused by the heterogeneity of the muon flux in different positions, a new flux correction method is proposed. Finally, three groups of simulation experiments are carried out with the help of Geant4 toolkit to optimize the algorithm parameters, verify the correction method and test the inspection quality under shielded condition, and compare this algorithm with another common inspection algorithm under different conditions. The results show that this algorithm can effectively identify and locate nuclear material with low misjudging and missing rates even when there is shielding and momentum precision is low, and the threshold correcting method is universally effective for density clustering algorithms

Explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers’ confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification

Pan-Cancer Analysis of Prognostic and Immune Infiltrates for CXCs

Background: CXCs are important genes that regulate inflammation and tumor metastasis. However, the expression level, prognosis value, and immune infiltration of CXCs in cancers are not clear. Methods: Multiple online datasets were used to analyze the expression, prognosis, and immune regulation of CXCs in this study. Network analysis of the Amadis database and GEO dataset was used to analyze the regulation of intestinal flora on the expression of CXCs. A mouse model was used to verify the fact that intestinal bacterial dysregulation can affect the expression of CXCs. Results: In the three cancers, multiple datasets verified the fact that the mRNA expression of this family was significantly different; the mRNA levels of CXCL3, 8, 9, 10, 14, and 17 were significantly correlated with the prognosis of three cancers. CXCs were correlated with six types of immuno-infiltrating cells in three cancers. Immunohistochemistry of clinical samples confirmed that the expression of CXCL8 and 10 was higher in three cancer tissues. Animal experiments have shown that intestinal flora dysregulation can affect CXCL8 and 10 expressions. Conclusion: Our results further elucidate the function of CXCs in cancers and provide new insights into the prognosis and immune infiltration of breast, colon, and pancreatic cancers, and they suggest that intestinal flora may influence disease progression through CXCs

China's response to a national land-system sustainability emergency

China has responded to a national land-system sustainability emergency via an integrated portfolio of large-scale programmes. Here we review 16 sustainability programmes, which invested US$378.5 billion (in 2015 US$), covered 623.9 million hectares of land and involved over 500 million people, mostly since 1998. We find overwhelmingly that the interventions improved the sustainability of China's rural land systems, but the impacts are nuanced and adverse outcomes have occurred. We identify some key characteristics of programme success, potential risks to their durability, and future research needs. We suggest directions for China and other nations as they progress towards the Sustainable Development Goals of the United Nations' Agenda 2030.Brett A. Bryan, Lei Gao, Yanqiong Ye, Xiufeng Sun, Jeffery D. Connor, Neville D. Crossman, Mark Stafford-Smith, Jianguo Wu, Chunyang He, Deyong Yu, Zhifeng Liu, Ang Li, Qingxu Huang, Hai Ren, Xiangzheng Deng, Hua Zheng, Jianming Niu, Guodong Han, Xiangyang Ho