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Green fractionation and a comprehensive overview of lignin molecular structures during the DES (deep eutectic solvent) pretreatment are very important for lignin valorization and the whole biorefinery process. Herein, intractable woody biomass (poplar wood) was pretreated with five types of carboxylic-acid-based DESs (acetamide served as an HBA (hydrogen bond acceptor), propanedioic acid, tartaric acid, malic acid, glutaric acid, and succinic acid served as HBDs (hydrogen bond donors)) under the optimized pretreatment conditions. Results showed that the optimal delignification ratio was achieved for tartaric-acid-based DES at 140 °C for 20 min under microwave-assisted heating. Two-dimensional HSQC NMR data demonstrated that the isolated poplar DES lignin consisted mainly of β-β, β-O-4 (normal and acylated forms), β-5, and esterified p-hydroxybenzoates (PBs) in different contents. Especially, the contents of β-O-4 in the isolated DES lignin fractions varied based on the pretreatment temperature and different chemical compositions of the DES. The antioxidant activity (2,2-diphenyl-1-picrylhydrazyl, DPPH analysis), microstructure (scanning electron microscope, SEM), and molecular weights (gel permeation chromatography, GPC) of the DES lignin fractions demonstrated that the DES delignification promoted the rapid assembly of lignin nanoparticles (LNPs) and could yield homogeneous lignin (1.23 < PDI < 1.58) with controlled nanometer size (30–170 nm) and good antioxidant activity. This study will improve the knowledge of structural changes of lignin during the different carboxylic-acid-based DES pretreatments and maximize the lignin valorization.

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF, the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.