An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Distinguishable RGE Running Effects Between Dirac Neutrinos and Majorana Neutrinos with Vanishing Majorana CP-Violating Phases

In a novel parametrization of neutrino mixing and in the approximation of т-lepton dominance, we show that the one-loop renormalization-group equations (RGEs) of Dirac neutrinos are different from those of Majorana neutrinos even if two Majorana CP-violating phases vanish. As the latter can keep vanishing from the electroweak scale to the typical seesaw scale, it makes sense to distinguish between the RGE running effects of neutrino mixing parameters in Dirac and Majorana cases. The differences are found to be quite large in the minimal supersymmetric standard model with sizable tanβ, provided the masses of three neutrinos are nearly degenerate or have an inverted hierarchy.     

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.     

Interpretation of Strengthening Mechanism of Densified Wood from Supramolecular Structures

In this study, densified wood was prepared by hot pressing after partial lignin and hemicellulose were removed through alkaline solution cooking. The tensile strength and elastic modulus of densified wood were improved up to 398.5 MPa and 22.5 GPa as compared with the original wood, and the characterization of its supramolecular structures showed that the crystal plane spacing of the densified wood decreased, the crystallite size increased, and the maximum crystallinity (CI) of cellulose increased by 15.05%; outstandingly, the content of O(6)H⋯O(3′) intermolecular H-bonds increased by approximately one-fold at most. It was found that the intermolecular H-bond content was significantly positively correlated with the tensile strength and elastic modulus, and accordingly, their Pearson correlation coefficients were 0.952 (p < 0.01) and 0.822 (p < 0.05), respectively. This work provides a supramolecular explanation for the enhancement of tensile strength of densified wood.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

Level Statistics Crossover of Chiral Surface States in a Three-Dimensional Quantum Hall System

Recent experiments have demonstrated the realization of the three-dimensional quantum Hall effect in highly anisotropic crystalline materials, such as ZrTe|_5 and BaMnSb_2. Such a system supports chiral surface states in the presence of a strong magnetic field, which exhibit a one-dimensional metal-insulator crossover due to suppression of surface diffusion by disorder potential. We study the nontrivial surface states in a lattice model and find a wide crossover of the level-spacing distribution through a semi-Poisson distribution. We also discover a nonmonotonic evolution of the level statistics due to the disorder-induced mixture of surface and bulk states.     

Compositions and Biological Activities of Pomegranate Peel Polyphenols Extracted by Different Solvents

Pomegranate peel extract (PPE), which is abundant in polyphenols, holds immerse prospects for the treatment of airway infection. In this study, water and ethanol of 30%, 50%, and 80% were used to prepare PPE. A total of 18 phenols belonging to 8 categories of polyphenols were identified in PPE by HPLC-MS/MS. The PPE from the four extraction solvents possessed different antioxidant, antibacterial, and anti-inflammatory activities. Principal component analysis revealed that though total flavonoids (TFs), total polyphenols (TPs), and total tannins (TTs) were responsible for the reducing power of PPE, only TFs contributed to the effect of PPE in inhibiting lipid membrane peroxidation. TPs, TTs, and punicalagin were positively correlated with the antibacterial strength against S. aureus while TTs alone contributed to the inhibition of methicillin-resistant S. aureus, implying the crucial role of TT in suppressing bacteria. Meanwhile, TTs was associated with the prevention of IL-6 release. The PPE with higher contents of TPs, TTs, and punicalagin had a weaker capacity to decrease nitric oxide secretion. PPE of 30% ethanol gained the highest integrated score due to its stronger antioxidant, antibacterial, and anti-inflammatory activities. It is a suitable candidate for the therapy of respiratory tract infection.     

Synthesis of Halopyrazole Matrine Derivatives and Their Insecticidal and Fungicidal Activities

Matrine is a traditional botanical pesticide with a broad-spectrum biological activity that is widely applied in agriculture. Halopyrazole groups are successfully introduced to the C13 of matrine to synthesize eight new derivatives with a yield of 78–87%. The insecticidal activity results show that the introduction of halopyrazole groups can significantly improve the insecticidal activity of matrine on Plutella xylostella, Mythimna separata and Spodoptera frugiperda with a corrected mortality rate of 100%, which is 25–65% higher than matrine. The fungicidal activity results indicate that derivatives have a high inhibitory effect on Ceratobasidium cornigerum, Cibberella sanbinetti, Gibberrlla zeae and Collectot tichum gloeosporioides. Thereinto, 4-Cl-Pyr-Mat has the best result, with an inhibition rate of 23–33% higher than that of matrine. Therefore, the introduction of halogenated pyrazole groups can improve the agricultural activity of matrine.     

On the Drinfeld-Sokolov hierarchy of type E(1)6 and its topological solution

In this paper we report some explicit evolutionary PDEs of the Drinfeld-Sokolov hierarchy of type E(1)6,and show how the unknown functions in these PDEs are rel...     

Chemical Constituents from the Flowers of Carthamus tinctorius L. and Their Lung Protective Activity

A new flavonoid, saffloflavanside (1), a new sesquiterpene, safflomegastigside (2), and a new amide, saffloamide (3), together with twenty-two known compounds (4–25), were isolated from the flowers of Carthamus tinctorius L. Their structures were determined based on interpretation of their spectroscopic data and comparison with those reported in the literature. The protective effects against lipopolysaccharide (LPS)-stimulated damage on human normal lung epithelial (BEAS-2B) cells of the compounds were evaluated using MTT assay and cellular immunofluorescence assay. The results showed that compounds 2–3, 8–11, and 15–19 exhibited protective effects against LPS-induced damage to BEAS-2B cells. Moreover, compounds 2–3, 8–11, and 15–19 can significantly downregulate the level of nuclear translocation of NF-κB p-p65. In summary, this study revealed chemical constituents with lung protective activity from C. tinctorius, which may be developed as a drug for the treatment of lung injury.