Hierarchical classification for improving parcel-scale crop mapping using time-series Sentinel-1 data.

Parcel-scale crop classification utilizing time-series satellite observations is of significant importance in precision agriculture. The prior knowledge that crop types can be organized in a hierarchical tree structure is beneficial for improving crop classification. Moreover, the crop hierarchy aligns with the coarse-to-fine cognitive process of geographic scenes. Based on the crop hierarchy, this study developed a general hierarchical classification framework for enhancing crop mapping using time-series Sentinel-1 data. Central to this method is a deep-learning-based hierarchical classification model that explores and makes use of crop hierarchical knowledge. First, preprocessed Sentinel-1 data were geometrically overlaid onto farmland parcel maps to derive parcel-scale time-series features. Second, we constructed a hierarchical crop type system for study areas based on the crop phenology of labeled crop-type samples. Third, we developed a deep-learning-based hierarchical classification model to identify crop types for each parcel, to generate final crop-type classification maps. The proposed approach was further discussed and verified through the implementation of parcel-scale time-series crop hierarchical classifications in a study area in France with farmland parcel maps and time-series Sentinel-1 data. The classification results, indicating significant improvements greater than 4.0% in overall accuracy and 5.0% in F1 score over comparative methods, demonstrated the effectiveness of the proposed method in learning multi-scale time-series features for hierarchical crop classification utilizing Sentinel-1 data sequences.

Solid-oil separation of coal tar residue to reduce polycyclic aromatic hydrocarbons via microwave-assisted extraction.

Coal tar residue (CTR) is acknowledged as hazardous industrial waste with high contents of carbon and toxic polycyclic aromatic hydrocarbons (PAHs). Microwave-assisted extraction for separating tar and residue in CTR was investigated to reduce the content of PAHs. The key operating factors such as solvent type, solvent addition amount, radiation temperature, and radiation time in the extraction process were evaluated. Results showed that extreme extraction performance in the solvent with cyclic structure was attained, and an enhancement in extraction efficiency was achieved in elevated solvent addition amount, radiation temperature, or radiation time in a certain range. The optimized conditions were determined as benzene was chosen as extractant, solvent-solid ratio of 5:1 mL/g, radiation temperature of 75 °C, and radiation time of 10 min. Relative extraction efficiency of CTR and reduction efficiency of 16 priority control PAHs were 28.70% and 92.82%, respectively. According to the characterizations of extracted residue (MCTR) and tar (MCT) under optimum experimental conditions, it is possible to convert them into value-added products (carbon materials, solid fuels, or chemicals). Solid-oil separation via microwave-assisted extraction is a safe and high-valued utilization approach for CTR.

Fatal poisoning due to aconite: Autopsy findings and postmortem quantitative analysis.

Aconitum species are commonly used in traditional Chinese medicine, and they have a narrow therapy window due to the possibility of aconitine poisoning. Aconitine poisoning deaths appear infrequently in forensic practice. It is important to collect valuable body samples in time due to the rapid absorption and excretion of aconitine. However, it is unknown whether postmortem samples have value for toxicological analysis if the deceased has experienced long-term treatment before death. Herein, we present a case of a woman who died after 12 days of failed active treatment for aconitine poisoning. Aconitine was detected in the liver tissue. To our knowledge, this is the first case report describing the detection of aconitine in a decedent after long-term active treatment. The findings indicated that the aconitine concentration in liver tissue can be maintained after long-term treatment; this information may therefore serve as a reference in forensic practice.

An anoikis-based signature for predicting prognosis in hepatocellular carcinoma with machine learning.

Background: Hepatocellular carcinoma (HCC) is a common malignancy with high mortality worldwide. Despite advancements in diagnosis and treatment in recent years, there is still an urgent unmet need to explore the underlying mechanisms and novel prognostic markers. Anoikis has received considerable attention because of its involvement in the progression of human malignancies. However, the potential mechanism of anoikis-related genes (ANRGs) involvement in HCC progression remains unclear. Methods: We use comprehensive bioinformatics analyses to determine the expression profile of ANRGs and their prognostic implications in HCC. Next, a risk score model was established by least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Then, the prognostic value of the risk score in HCC and its correlation with clinical characteristics of HCC patients were further explored. Additionally, machine learning was utilized to identify the outstanding ANRGs to the risk score. Finally, the protein expression of DAP3 was examined on a tissue microarray (TMA), and the potential mechanisms of DAP3 in HCC was explored. Results: ANRGs were dysregulated in HCC, with a low frequency of somatic mutations and associated with prognosis of HCC patients. Then, nine ANRGs were selected to construct a risk score signature based on the LASSO model. The signature presented a strong ability of risk stratification and prediction for overall survival in HCC patients.Additionally, high risk scores were closely correlated with unfavorable clinical features such as advanced pathological stage, poor histological differentiation and vascular invasion. Moreover, The XGBoost algorithm verified that DAP3 was an important risk score contributor. Further immunohistochemistry determined the elevated expression of DAP3 in HCC tissues compared with nontumor tissues. Finally, functional analyses showed that DAP3 may promote HCC progression through multiple cancer-related pathways and suppress immune infiltration. Conclusion: In conclusion, the anoikis-based signature can be utilized as a novel prognostic biomarker for HCC, and DAP3 may play an important role in the development and progression of HCC.