FTIR characteristics of charcoal with different combustion degrees as an indication of the genesis by and their significances for formation of fusinite in coal

Fourier transform infrared spectroscopy (FTIR), as a non-destructive method, is widely used for the identification of compounds and the characterization of molecular structures. In order to characterize the changes in the chemical structure of charcoal under different combustion temperatures, and thus to provide a theoretical basis for the formation of fusinite in coal, plant samples (charcoal) from modern wildfires with different degrees of combustion were selected to quantify their chemical structures using FTIR. The results shown that the sample reflectance was positively proportional to the combustion temperature. The sample No. 1 with maximum combustion temperature had the highest degree of combustion, which was measured to reach 518 ℃. The aromatic structure was dominated by tri-substituted benzene rings in all samples except the highest combustion sample No. 1, but dehydrocondensation occurred with increasing combustion temperature, resulting in a reduction of tri-substituted content of benzene rings to 20.5%. The tetra-substituted content was elevated due to dehydroaromatization of the naphthenic structure, while the change in the penta-substituted content was related to the cyclization of aliphatic chain and the decarboxylation of benzene ring. With the increase of combustion temperature, the CC content gradually increased due to the formation of aromatic hydrocarbons or the shedding of molecular side chains after dehydrogenation of cycloalkanes, reached 32% in the sample No. 1. The content of C－O first decreased and then increased. In the sample No. 1, the content of alkyl ether and aryl ether was the lowest, and the content of phenolic hydroxyl group was the highest, which may be the generation of phenolic substances by thermal breakage of ether bond under high temperature combustion. The CO content increased and then decreased to as low as 5.6% in the sample No. 1, which was due to the poor stability of the bond. Due to the influence of combustion temperature, the content of fatty substances varied greatly, with an overall gradual increase in methylene content, a decrease in methyl group, and an increase in branching degree. There were five types of hydrogen bonds in the samples, with ether-oxygen hydrogen bonds predominating in samples affected by low temperature (>55%). Cyclic hydrogen bonds and hydroxyl-N hydrogen bonds appeared in sample No. 1, while the content of ether-oxygen hydrogen bonds decreased significantly to 13.2%, which was attributed to the reduction of oxygen-containing functional groups caused by the increasing temperature. Comparison of reflectance and FTIR characteristics of fusinite in coal revealed that the characteristics of fusinite (semifusinite) in coal were very similar to those of charcoal, which might be produced mainly by wildfires. These changes indicated the effect of combustion temperature on the chemical structure in charcoal, reflecting the process of organic molecular structure changed with temperature in charcoal, and providing a theoretical basis for the evolution of organic matter and the formation of fusinite in coal

Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification

In this paper, we present a new deep learning model to classify hematoxylin–eosin-stained breast biopsy images into four classes (normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas). Our model uses a parallel structure consist of a convolutional neural network (CNN) and a recurrent neural network (RNN) for image feature extraction, which is greatly different from the common existed serial method of extracting image features by CNN and then inputting them into RNN. Then, we introduce a special perceptron attention mechanism, which is derived from the natural language processing (NLP) field, to unify the features extracted by the two different neural network structures of the model. In the convolution layer, general batch normalization is replaced by the new switchable normalization method. And the latest regularization technology, targeted dropout, is used to substitute for the general dropout in the last three fully connected layers of the model. In the testing phase, we use the model fusion method and test time augmentation technology on three different datasets of hematoxylin–eosin-stained breast biopsy images. The results demonstrate that our model significantly outperforms state-of-the-art methods

A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China

The continuous temperature rise has raised global concerns about CO2 emissions. As the country with the largest CO2 emissions, China is facing the challenge of achieving large CO2 emission reductions (or even net-zero CO2 emissions) in a short period. With the strong support and encouragement of the Chinese government, technological breakthroughs and practical applications of carbon capture, utilization, and storage (CCUS) are being aggressively pursued, and some outstanding accomplishments have been realized. Based on the numerous information from a wide variety of sources including publications and news reports only available in Chinese, this paper highlights the latest CCUS progress in China after 2019 by providing an overview of known technologies and typical projects, aiming to provide theoretical and practical guidance for achieving net-zero CO2 emissions in the future

A Review of the Metallogenic Mechanisms of Sandstone-Type Uranium Deposits in Hydrocarbon-Bearing Basins in China

As a valuable mineral resource, uranium is extensively utilized in nuclear power generation, radiation therapy, isotope labeling, and tracing. In order to achieve energy structure diversification, reduce dependence on traditional fossil fuels, and promote the sustainable development of energy production and consumption, research on the metallogenic mechanisms and related development technologies of uranium resources has been one of the focuses of China’s energy development. Sandstone-type uranium deposits make up approximately 43% of all deposits in China, making them the most prevalent form of uranium deposit there. Sandstone-type uranium deposits and hydrocarbon resources frequently coexist in the same basin in China. Therefore, this study summarizes the spatial and chronological distribution, as well as the geological characteristics, of typical sandstone-type uranium deposits in China’s hydrocarbon-bearing basins. From the perspectives of fluid action, geological structure, and sedimentary environment, the metallogenic mechanisms of sandstone-type uranium deposits in hydrocarbon-bearing basins are explored. According to the research, the rapid reduction effect of oil and gas in the same basin is a major factor in the generation of relatively large uranium deposits. Additionally, ions such as CO32− and HCO3− in hydrothermal fluids of hydrocarbon-bearing basins, which typically originate from dispersed oil and gas, are more conducive to uranium enrichment and sedimentation. This study provides guidance for efficient sandstone-type uranium deposit exploration and production in hydrocarbon-bearing basins and helps to achieve significant improvements in uranium resource exploitation efficiency

Meta-Analysis Based on Clinical RCTs: The Effect of Molecular Epimerism on the Safety of Glycyrrhizic Acid

Objective. To carry out the meta-analysis on the clinical safety of glycyrrhizic acid and the influencing factors between 18α-glycyrrhizinate (18α-GL) and 18β-glycyrrhizinate (18β-GL). Methods. Magnesium isoglycyrrhizinate injection was used as the representative preparation of 18α-GL, and compound glycyrrhizin injection was used as the representative preparation of 18β-GL. The clinical control trial of magnesium isoglycyrrhizinate injection and compound glycyrrhizin injection was searched in a computer, which was published from January 2006 to December 2019 on the databases such as PubMed, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (CSTJ), and Wanfang Medical Network (Wanfang Data). The data associated with adverse drug reactions (ADRs) were extracted. RevMan5.3 was used for statistical analysis. Results. Finally, 24 studies were included, and 2757 patients were involved, of which the experimental group was mainly treated with magnesium isoglycyrrhizinate, while the control group was mainly treated with compound glycyrrhizin. The results showed that the occurrence of ADRs was significantly lower in the experimental group than that in the control group, and the difference between two groups was statistically significant (RR = 0.26, 95% CI = (0.18, 0.38), P<0.00001). There was no heterogeneity among the studies (I2 = 0%, P=1.00). Conclusion. Compared with 18β-GL, 18α-GL had a lower incidence of adverse reactions and better clinical safety

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset