Yanli Zhu

Abstract Municipal solid waste (MSW) severely threatens human health and the ecological environment owing to its toxicity, mutagenic activity and carcinogenicity. The continuous increase in MSW together with stringent regulations makes sanitary disposal imperative. Waste sorting and recycling has been recognized as an efficient and economical treatment strategy. By analysing research data from 31 provinces between 2000 and 2017, the overarching goal of this work was to determine the characterizations and the compositions of MSW in China and then provide advices for sorting, transporting, storing and disposing of MSW. The results showed that the amount of MSW that was generated ranged from 0.08 to 2.34 kg d −1 ca −1 and averaged 0.73 kg d −1 ca −1 in China. The average bulk density, moisture content and the wet basis of the low calorific value of the MSW were 325 kg m −3 , 50.3% and 4649 kcal kg −1 . The MSW in China could be classified into four main categories, food waste, recycling waste, landfill waste and hazardous substances, and could be further classified into ten sub-categories. Overall, food waste was the most common and could be best managed via compost production. Bulk density was highly positively correlated with the ratio of the dust and bricks in all MSW and highly negatively correlated with the ratio of the food waste, metal, glass, plastic and rubber. The wet basis of the low calorific value was highly positively correlated with the ratio of the plastic and rubber, and the water content was highly positively correlated with the ratio of the food waste. Temporally, most of the components, especially waste paper and plastics, increased, while wood, dust and bricks decreased. Graphic abstract

Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer characterized by rapid tumor growth and early metastasis. Accurate prediction of prognosis and therapeutic response is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we conducted a deep-learning analysis of Hematoxylin and Eosin (H&E) stained histopathological images using contrastive clustering and identified 50 intricate histomorphological phenotype clusters (HPCs) as pathomic features. We identified two of 50 HPCs with significant prognostic value and then integrated them into a pathomics signature (PathoSig) using the Cox regression model. PathoSig showed significant risk stratification for overall survival and disease-free survival and successfully identified patients who may benefit from postoperative or preoperative chemoradiotherapy. The predictive power of PathoSig was validated in independent multicenter cohorts. Furthermore, PathoSig can provide comprehensive prognostic information beyond the current TNM staging system and molecular subtyping. Overall, our study highlights the significant potential of utilizing histopathology images-based deep learning in improving prognostic predictions and evaluating therapeutic response in SCLC. PathoSig represents an effective tool that aids clinicians in making informed decisions and selecting personalized treatment strategies for SCLC patients.

The method of thermogravimetry/differential scanning calorimetry–mass spectrometry–Fourier transform infrared (TG/DSC-MS-FTIR) simultaneous analysis has been used to study thermal decomposition of ammonium perchlorate (AP). The processing of nonisothermal data at various heating rates was performed using NETZSCH Thermokinetics. The MS-FTIR spectra showed that N2O and NO2 were the main gaseous products of the thermal decomposition of AP, and there was a competition between the formation reaction of N2O and that of NO2 during the process with an iso-concentration point of N2O and NO2. The dependence of the activation energy calculated by Friedman's iso-conversional method on the degree of conversion indicated that the AP decomposition process can be divided into three stages, which are autocatalytic, low-temperature diffusion and high-temperature, stable-phase reaction. The corresponding kinetic parameters were determined by multivariate nonlinear regression and the mechanism of the AP decomposition process was proposed.