Search

Silibinin is a flavonoid extracted from the medicinal plant Silybum marianum (milk thistle), traditionally used to treat liver disease. Recent studies have shown that the antioxidative stress and anti-inflammatory effects of milk thistle are used in the treatment of neurological diseases. Silibinin has antioxidative stress and antiapoptotic effects and reduces cognitive impairment in models of Alzheimer’s disease (AD). However, the underlying mechanism of silibinin related to improvement of cognition remains poorly understood. In this study, we used the model of lateral ventricle injection of formaldehyde to examine the related mechanism of silibinin in improving cognitive impairment disorders. Oral administration of silibinin for three weeks significantly attenuated the cognitive deficits of formaldehyde-induced mice in a Y -maze test and Morris water maze test. Y -maze results show that silibinin increases the rate of spontaneous response alternation in FA-induced mice. Silibinin increases the target quadrant spending time and decreases escape latency in the Morris water maze test. We examined the effect of silibinin on the NRF2 signaling pathway, and silibinin promoted the nuclear transfer of NRF2 and increased the expression of HO-1 but did not significantly increase the protein expression of NRF2 in the hippocampus. Well, silibinin reduces the content of DHE and decreases the levels of apoptosis of mature neuron cells. We investigated the effect of silibinin on the content of formaldehyde degrading enzymes; biochemical analyses revealed that silibinin increased GSH and ALDH2 in formaldehyde-induced mice. In addition, as one of the pathological changes of AD, TAU protein is also hyperphosphorylated in FA model mice. Silibinin inhibits the expression of GSK-3β in model mice, thereby reducing the phosphorylation of TAU proteins ser396 and ser404 mediated by GSK3β. Based on our findings, we verified that the mechanism of silibinin improving cognitive impairment may be antioxidative stress, and silibinin is one of the potentially promising drugs to prevent formaldehyde-induced cognitive impairment.

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been developed in recent years to tackle this issue, but many of them merely focus on reproducing the binding affinity of known binders. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input, and it was trained through a multi-objective process with three related tasks, including deriving the protein–ligand binding affinity, protein–ligand contact map, and intra-ligand distance matrix. To serve those tasks, a large number of decoy non-binders were selected and added to the standard PDBbind data set. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to other deep learning models that rely on 3D protein–ligand complex structures as input. It also showed notably better performance in virtual screening trials on the DUD-E and LIT-PCBA benchmark. In particular, PLANET achieved comparable accuracy on LIT-PCBA as the conventional docking program Glide. However, it only took less than 1% of the computation time required by Glide to finish the same job because it did not perform exhaustive conformational sampling. In summary, PLANET exhibited a decent performance in binding affinity prediction as well as virtual screening, which makes it potentially useful for drug discovery in practice.

Traditional proper orthogonal decomposition (POD)-Galerkin projection for reduced-order models (ROMs) of fluid flows is based on a fixed domain. The current method removes this limitation by consid...

Aloe emodin attenuated motor and cognitive dysfunction in HD model mice. Aloe emodin decreased the level of the mutant huntingtin protein and inhibited CaMKII/Smad and TGF-β1/Smad signaling.

The process of seismic multiple removal in oil seismic exploration is crucial for the imaging of underground structures with primary reflections. The inclusion of prediction and subtraction in the multiple removal method requires adaptive subtraction to remove the complex differences between the true and modeled multiples. The traditional adaptive subtraction method is generally expressed as a linear regression (LR) problem. In this article, we introduce U-net, a popular deep learning tool, to represent the complex differences between the true and modeled multiples in a nonlinear relationship. Thus, we present adaptive subtraction as a non-LR problem. The modeled multiples and full recorded seismic response with multiples and primaries are used as the input and labels to train U-net. The proposed U-net method is able to avoid over-fitting of the primaries due to the sufficient number of 2-D data windows for the training of U-net, as well as the network parameter regularization and the L1 norm minimization constraint on the primaries. The proposed U-net method attains 20.5 dB and 2.5 dB improvement in the signal-to-noise ratio (SNR) using the first synthetic data and second synthetic (Sigsbee2B) data set, respectively, compared with the traditional LR method, and a qualitative improvement for a real data set test.

The first mercury-catalyzed synthesis of 1,2-diketones by alkyne oxidation has been developed. This inexpensive method extends the potential of mercury catalysis and allows the rapid construction of various 1,2-diketones and α-carbonyl amides in good yields with high functional group tolerance.

Recently, charged aerosols are used widely in many applications, such as fog elimination, inactivation of airborne viruses and so on. Atmospheric pressure non‐equilibrium plasma (APNP) have become an efficient way to generate charged aerosols. Except the traditional pin‐plate or wire‐plate APNP sources, the new APNP sources such as large scale corona discharge system and air plasma jet array are introduced. These sources can increase the ion density in open air and generate adjustable large plasma plume, which are helpful in increasing the charging efficiency on aerosols. Although the interactions between plasma and aerosols is quite complicated, the preliminary study suggests that the diffusion charging dominates aerosols below 0.1 μm, field charging dominates aerosols larger than 1 μm, and the photo charging also contributes to aerosols charging. These reactive charged aerosols have shown their potential in the artificial rain enhancement, biomedicine, and material processing. Finally, challenges and opportunities for theoretical, experimental, and application research in related cross‐disciplinary areas are presented to stimulate critical discussions and collaborations in the future.

Rain enhancement is the main activity field of weather modification in the countries that suffer from water stress. The charged aerosol is a new man-made condensation nucleus to enhance the water vapor condensation. In this article, the negative-polarity air plasma jet array with plasma volume larger than 2 cm3 was used to generate the charged aerosols. Aerosols are mainly charged in the unipolar atmosphere. The averaged charge number of charged aerosols was measured to be 5818e. The attractive electric force of charged aerosols was more than ten times higher than the sum of thermophoretic forces and diffusophoretic forces. These charged aerosols accelerated the collision–coalescence process inside the cloud chamber, generated large droplets in the range of 40– $70~\mu \text{m}$ , and increased the settling amount of moisture by 58.8%.

Two series of thiophene-based copolymers with cyclodextrins (CDs) grafted on side chain (OH-β-CD-P3HT and Ph-β-CD-P3HT) were synthesized via click reaction, in which the pendant CDs and the polythiophenes (PTs) main chain afford stereo-selective interactions and high conductivity respectively. The copolymers have been employed to construct an electrochemical sensor to achieve efficient enantio-recognition for amino acids. The OH-β-CD-P3HT and Ph-β-CD-P3HT shows reverse interaction preference for D- and L-isomer due to the different interaction mode of native and substituted CDs with chiral guest. It is found that, higher content of block with CD in the copolymer favors lower LOD because of the specific inclusion abilities of CD, while lower content of block with CD is beneficial to chiral selectivity related to the higher structural flexibility of the polymer. The electrochemical sensor was also proven to be capable of determining the relative content of L- or D-isomer in an enantiomer pair.

Using the idea of big data analysis, this paper makes a quantitative analysis of the development of characteristic towns in China based on two dimensions of space and type, static and dynamic, and puts forward policy suggestions. The results showed that: distribution density of characteristic towns in China is dense and stratified, which decreases from east to west. There are significant differences in distribution density between different types of towns. The development of small towns follows the evolution trajectory of “enlightenment-initial development-high speed development-normative construction” and forms a ladder transfer pattern of “Zhejiang-southeast coast-national-southeast coast”. Town construction is highly resource embeddedness, and is undergoing a transition from resource-driven to innovation-driven.

The optimal intermodal nodes and routes are two of the most challenging issues for intermodal participants. We present a two-phase approach that includes the fuzzy c-means clustering method (FCM) and a multi-objective optimization model to solve intermodal location-routing issues. A weighted sum technique and a genetic algorithm (GA) are designed to address this model. The two-phase approach is beneficial in meeting different market demand preferences of intermodal participants. It also has applications in solving the sustainable intermodal location-routing problems, further solving the network optimization problem in large-scale scenarios. A typical intermodal transport network in the Bohai Rim region is used to verify the effectiveness of this approach. The results provide references for the participants in the Bohai Rim region to choose the optimal intermodal nodes and routes. The findings also offer theoretical insights for optimizing intermodal networks in other regions of China, with goals of improving sustainable transport efficiencies.

Adaptive multiple subtraction is an important step for the success of multiple removal after multiple prediction. Generally, the traditional method uses a 2D matching filter to combine the predicted multiples to match with the original data directly. Due to the complicated mismatches between the predicted multiples and true multiples, multiples may be removed aggressively with damaging primaries and vice versa for the traditional method. Especially in complex media, how to balance multiple removal and primary preservation is very important. In this paper we propose to use multi feature-gathers of the predicted multiples for adaptive multiple subtraction. The feature of the predicted multiples is extracted by the convolutional neural network with the predicted multiples as the input and the original data as the output. The multi feature-gathers of the predicted multiples contain more prediction information than the predicted multiples themselves. Therefore, the multi feature-gathers combined by a 3D matching filter can better match with the true multiples than the predicted multiples themselves combined by a 2D matching filter. Synthetic and field data examples demonstrate that the proposed method can better balance multiple removal and primary preservation than the traditional method.

In combination with short circuit simulation and wavelet packet theory, the corresponding probabilistic analysis on the characteristics of single-phase grounding faults of distribution network under stochastic context is studied in this paper. There are two pieces of major research works: firstly, the Latin hypercube sampling (LHS) method is adopted to accelerate probabilistic short circuit analysis, thus improving the efficiency for a further implementation; secondly, wavelet packet method is employed to investigate the influence of stochastic factors on the characteristics of short circuit and fault line identification. Test results verify the effects of the works, as well as some conclusions are drawn.

The COVID-19, viral influenza, tuberculosis, and other widespread infectious diseases evidence that pathogenic biological aerosols (PBAs) are a serious threat to public health. Different from traditional inactivation methods, such as ultraviolet (UV) light which are only safe to use when people are not present, and high-efficiency particulate filters (HEPA) which merely filter microbes without killing them, atmospheric pressure nonequilibrium plasma (APNP) has shown its tremendous potential in drastically diminishing the aerosol transmission route of the infectious agents through the abatement of PBAs. The key issues to develop high performance APNP based air purification system are critically reviewed. Systematic studies on the hazards of different PBAs and the spread of PBAs in indoor environments guide the development of APNP sources to control communicable diseases. The key six sampling and seven detection methods on PBAs are introduced to analyze the PBA abatement efficiency by APNP. Seven common APNP sources which can remove viruses and bacteria aerosols efficiently developed during the past 8 years are introduced. For the APNP sources with small plasma volume, the electric field and diffusion driven charging are the dominant mechanisms to charge PBAs, while the common methods of dusty plasma research can be adapted to atmospheric-pressure conditions to describe the charging effects of APNP sources with large plasma volume. Plentiful long- and short-lifetime reactive oxygen and nitrogen species (RONS) generated by APNP effectively contribute to inactivation of bacterial aerosols. Current studies suggest that viral aerosols are mainly inactivated by short-lifetime RONS including 1O2, ONOO− and ONOOH. The study on the dissolution and reaction of gaseous RONS in microdroplets and accurate measurements on the evolution of charged PBAs are envisaged to be the focus of future research. Opportunities for multidisciplinary collaborative research to advance the development of next-generation high-performance plasma-based air purifiers are highlighted.

In this paper, based on probability modeling, Monte Carlo simulation is adopted to conduct the probabilistic short circuit analysis on the single-phase grounding faults of distribution network. Firstly, a distribution network with four feeders and a photovoltaic (PV) generation is built in Matlab/ Simulink. Subsequently, the randomness of fault location, transition resistance, load and PV generation are modeled. Lastly, the specific impacts of these probabilistic uncertainties on the zero-sequence current of such fault line in the single-phase grounding condition are investigated and studied, as well as the corresponding conclusions are drawn.

In recent years, plasma medicine has attracted more and more attentions with the fast development of atmospheric plasmas technology. The experimental measurements have definitely shown some positive effects on the biological tissue and materials, however, the mechanisms of interactions between atmospheric plasma and cells are still unknown. In this paper, we coupled the fluid model and multi-layer dielectric of spherical cell to investigate the temporal-spatial distribution of electric fields in the cells induced by the atmospheric plasmas generated by dielectric barrier discharges. From the simulation data, the voltage on the cell almost totally drops on plasma membrane and the voltage on the plasma membrane is much larger than that of normal cells. On the other hand, both the transmembrane potentials of inner and outer membrane rise very fast during the process of ignition, then the former drops quickly after the breakdown, but the later sustains at a relatively large value until the next breakdown event. The influences on the voltage distribution in cell of applied voltage and excitation frequency are also discussed based on the simulation data. At present, the numerical investigations on the interaction of plasma and cells still need to be greatly developed, and the given simulation in this work will show some insights to the further theoretical study.

In this paper, we extended the application of POD-Galerkin projection for model order reduction from usual fixed-domain problems to more general fluid-solid systems when moving boundary/interface is involved. The idea is similar to numerical simulation approaches using embedded force terms to represent boundary motion and domain change. The embedded boundary allows a globally defined fixed domain including both fluid and solid, where POD-Galerkin projection can be directly applied. However, such a modified approach will not get away with the unsteadiness of boundary terms which appear as time-dependent coefficients in the new Galerkin model. These coefficients need to be precomputed for prescribed periodic motion, or worse, to be computed at each time step for non-prescribed (e.g. with fluid-structure interaction) or non-periodic situation. Though the time cost of computing these unsteady coefficients is smaller, as the associated integration is only in the close neighborhood of moving boundaries, it is still much higher than typical Galerkin model with constant coefficients. The extra computational cost gets expensive in some cases and eventually undermines the value of using reduced-order models. The further study shows, however, that the most expensive integration resulted from the unsteady motion has almost negligible impact on the overall dynamics. Dropping these expensive terms reduces the computation cost by at least one order while no obvious effect on model accuracy is noticed. A more aggressive approach is to decompose the moving boundary/domain to orthogonal modes and derive another low-order model with fixed coefficients for boundary motion. With domain decomposition, we have two coupled low-order models both with fixed coefficients and therefore the combined model is fast. Though the model with domain decomposition is less accurate at the boundary, it seems a fair trade-off for its benefit on computational cost saving.