Pollution sources and risk assessment of potentially toxic elements in soils of multiple land use types in the arid zone of Northwest China based on Monte Carlo simulation.

The concentration of potentially toxic elements (PTEs) in soils of different land-use types varies depending on climatic conditions and human. Topsoil samples were collected in Northwest China to investigate PTE pollution and risk in different land uses, and thereby estimate the risk of various pollution sources. The results showed that human activity had an impact on PTE concentrations in the study area across all land use types, with farmland, grassland, woodland, and the gobi at moderate pollution levels and the desert at light pollution levels. Different PTE sources pose different risks depending on the land-use type. Apart from deserts, children are exposed to carcinogenic risk from a variety of sources. A mixed natural and agricultural source was the main source of public health risk in the study area, contributing 38.7% and 39.0% of the non-carcinogenic and 40.7% and 35.5% of the carcinogenic risks, respectively. Monte Carlo simulations showed children were at a higher health risk from PTEs than adult s under all land uses, which ranked in severity as farmland > woodland > grassland > gobi > desert. As and Ni has a higher probability of posing both a non-carcinogenic and a carcinogenic risk to children. Sensitivity analysis showed that the contribution of parameters to the assessment model of PTEs exhibited the following contribution pattern: concentration > average body weight > ingestion rate > other parameters. The PTEs affecting the risk assessment model were not common among different land use types, where the importance distribution pattern of each parameter was basically the same in woodland, grassland, and farmland, and Ni contributed the most to carcinogenic risk. However, Cr contributed the most to the carcinogenic risk in the desert and gobi.

[Diagnostic Study of Multi-spectral Intelligent Analyzer in Diagnosis of 
the Infiltration Degree of Lung Adenocarcinoma].

BACKGROUND: Lung cancer is one of the most common malignant tumors in the world. The accuracy of intraoperative frozen section (FS) in the diagnosis of lung adenocarcinoma infiltration cannot fully meet the clinical needs. The aim of this study is to explore the possibility of improving the diagnostic efficiency of FS in lung adenocarcinoma by using the original multi-spectral intelligent analyzer. METHODS: Patients with pulmonary nodules who underwent surgery in the Department of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University from January 2021 to December 2022 were included in the study. The multispectral information of pulmonary nodule tissues and surrounding normal tissues were collected. A neural network model was established and the accuracy of the neural network diagnostic model was verified clinically. RESULTS: A total of 223 samples were collected in this study, 156 samples of primary lung adenocarcinoma were finally included, and a total of 1,560 sets of multispectral data were collected. The area under the curve (AUC) of spectral diagnosis in the test set (10% of the first 116 cases) of the neural network model was 0.955 (95%CI: 0.909-1.000, P<0.05), and the diagnostic accuracy was 95.69%. In the clinical validation group (the last 40 cases), the accuracy of spectral diagnosis and FS diagnosis were both 67.50% (27/40), and the AUC of the combination of the two was 0.949 (95%CI: 0.878-1.000, P<0.05), and the accuracy was 95.00% (38/40). CONCLUSIONS: The accuracy of the original multi-spectral intelligent analyzer in the diagnosis of lung invasive adenocarcinoma and non-invasive adenocarcinoma is equivalent to that of FS. The application of the original multi-spectral intelligent analyzer in the diagnosis of FS can improve the diagnostic accuracy and reduce the complexity of intraoperative lung cancer surgery plan.
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Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study.

Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard plights in training deep architectures such as vanishing gradients and overfitting, it also uniquely suffers from over-smoothing, information squashing, and so on, which limits their potential power for encoding the high-order neighbor structure in large-scale graphs. Although numerous efforts are proposed to address these limitations, such as various forms of skip connections, graph normalization, and random dropping, it is difficult to disentangle the advantages brought by a deep GNN architecture from those "tricks" necessary to train such an architecture. Moreover, the lack of a standardized benchmark with fair and consistent experimental settings poses an almost insurmountable obstacle to gauge the effectiveness of new mechanisms. In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs. We categorize existing approaches, investigate their hyperparameter sensitivity, and unify the basic configuration. Comprehensive evaluations are then conducted on tens of representative graph datasets including the recent large-scale Open Graph Benchmark, with diverse deep GNN backbones. We demonstrate that an organic combo of initial connection, identity mapping, group and batch normalization attains the new state-of-the-art results for deep GNNs on large datasets. Codes are available: https://github.com/VITA-Group/Deep_GCN_Benchmarking.

Epidemiology of esophageal cancer in 2020 and projections to 2030 and 2040.

Esophageal cancer is a familiar malignancy with high incidence and mortality, and the overall prognosis is poor. The numbers of cases of and deaths from esophageal cancer have risen rapidly in recent decades. It is one of the most malignant cancers, with more than 0.6 million new cases and 0.54 million deaths worldwide in 2020. Here, we present the global epidemiology of esophageal cancer in 2020 and projections to 2030 and 2040 at different geographical levels of continents, regions and countries, and analyze them by gender, race, geographic region and human development index. We summarize the prospects for the esophageal cancer burden and risk factors in different areas, which will be useful for global esophageal cancer clinical therapy and cancer control planning.

[Multivariate Analysis of Solid Pulmonary Nodules Smaller than 1 cm in 
Distinguishing Lung Cancer from Intrapulmonary Lymph Nodes].

BACKGROUND: Preoperative diagnosis and differential diagnosis of small solid pulmonary nodules are very difficult. Computed tomography (CT), as a common method for lung cancer screening, is widely used in clinical practice. The aim of this study was to analyze the clinical data of patients with malignant pulmonary nodules and intrapulmonary lymph nodes in the clinical diagnosis and treatment of <1 cm solid pulmonary nodules, so as to provide reference for the differentiation of the two. METHODS: Patients with solid pulmonary nodules who underwent surgery from June 2017 to June 2020 were analyzed retrospectively. The clinical data of 145 nodules (lung adenocarcinoma 60, lung carcinoid 2, malignant mesothelioma 1, sarcomatoid carcinoma 1, lymph node 81) were collected and finally divided into two groups: lung adenocarcinoma and intrapulmonary lymph nodes, and their clinical data were statistically analyzed. According to the results of univariate analysis (χ² test, t test), the variables with statistical differences were selected and included in Logistic regression multivariate analysis. The predictive variables were determined and the receiver operating characteristic (ROC) curve was drawn to get the area under the curve (AUC) value of the area under the curve. RESULTS: Logistic regression analysis showed that the longest diameter, Max CT value, lobulation sign and spiculation sign were important indicators for distinguishing lung adenocarcinoma from intrapulmonary lymph nodes, and the risk ratios were 106.645 (95%CI: 3.828-2,971.220, P<0.01), 0.980 (95%CI: 0.969-0.991, P<0.01), 3.550 (95%CI: 1.299-9.701, P=0.01), 3.618 (95%CI: 1.288-10.163, P=0.02). According to the results of Logistic regression analysis, the prediction model is determined, the ROC curve is drawn, and the AUC value under the curve is calculated to be 0.877 (95%CI: 0.821-0.933, P<0.01). CONCLUSIONS: For <1 cm solid pulmonary nodules, among many factors, the longest diameter, Max CT value, lobulation sign and spiculation sign are more important in distinguishing malignant pulmonary nodules from intrapulmonary lymph nodes.

Efficiency of a multi-soil-layering system on wastewater treatment using environment-friendly filter materials.

The multi-soil-layering (MSL) system primarily comprises two parts, specifically, the soil mixture layer (SML) and the permeable layer (PL). In Japan, zeolite is typically used as the permeable layer material. In the present study, zeolite was substituted with comparatively cheaper and more environmentally friendly materials, such as expanded clay aggregates, oyster shells, and already-used granular activated carbon collected from water purification plants. A series of indoor tests indicated that the suspended solid (SS) removal efficiency of granular activated carbon was between 76.2% and 94.6%; zeolite and expanded clay aggregates achieved similar efficiencies that were between 53.7% and 87.4%, and oyster shells presented the lowest efficiency that was between 29.8% and 61.8%. Further results show that the oyster shell system required an increase of wastewater retention time by 2 to 4 times that of the zeolite system to maintain similar chemical oxygen demand (COD) removal efficiency. Among the four MSL samples, the zeolite system and granular activated carbon system demonstrated a stable NH3-N removal performance at 92.3%-99.8%. The expanded clay aggregate system present lower removal performance because of its low adsorption capacity and excessively large pores, causing NO3--N to be leached away under high hydraulic loading rate conditions. The total phosphorous (TP) removal efficiency of the MSL systems demonstrated no direct correlation with the permeable layer material. Therefore, all MSL samples achieved a TP efficiency of between 92.1% and 99.2%.