Effects of SGSF043 on the Germination Activity of Chinese Cabbage Seeds: Evidence from Phenotypic Indicators, Stress Resistance Indicators, Hormones and Functional Genes.

In this study, the effect of spp. on the seed germination of cabbage, a cruciferous crop, was investigated. The effects of this strain on the seed germination vigor, bud growth and physiological characteristics of Chinese cabbage were analyzed by a seed coating method.

A new Bowman-Birk type protease inhibitor regulated by MeJA pathway in maize exhibits anti-feedant activity against the Ostrinia furnacalis.

Jasmonic acid (JA), an important plant hormone, plays a crucial role in defending against herbivorous insects. In this study, we have identified a new Bowman-Birk type protease inhibitor (BBTI) protein in maize that is regulated by the JA pathway and exhibits significant antifeedant activity, which is notably induced by exogenous Methyl Jasmonate and Ostrinia furnacalis feeding treatments. Bioinformatics analysis revealed significant differences in the BBTI protein among different maize inbred lines, except for the conserved domain.

PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training.

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors.

Salicylic acid inducing the expression of maize anti-insect gene SPI: a potential control strategy for Ostrinia furnacalis.

Background: Due to being rooted in the ground, maize (Zea mays L.) is unable to actively escape the attacks of herbivorous insects such as the Asian corn borer (Ostrinia furnacalis). In contrast to the passive damage, plants have evolved defense mechanisms to protect themselves from herbivores.

Magnetic Adsorbent FeO/ZnO/LC for the Removal of Tetracycline and Congo Red from Aqueous Solution.

Zeolitic imidazolate frameworks (ZIFs) can be used as an adsorbent to efficiently adsorb organic pollutants. However, ZIF nanoparticles are easy to form aggregates, hampering the effective and practical application in practical adsorption. In this study, the ZIF-8 was successfully loaded onto lignocellulose (LC) to further produce ZnO/LC by in situ growth method and hydrothermal treatment, and then FeO nanoparticles (FeO NPs) were loaded onto ZnO/LC to prepare magnetic FeO/ZnO/LC adsorbent for removing tetracycline (TC) and congo red (CR) pollutants from aqueous solution.

Simple ultrasonic integration of shapeable, rebuildable, and multifunctional MIL-53(Fe)@cellulose composite for remediation of aqueous contaminants.

Metal-organic frames (MOFs) have been recognized as one of the best candidates in the remediation of aqueous contaminants, while the fragile powder shape restricts the practical implementation. In this work, a shapeable, rebuildable, and multifunctional MOF composite (MIL-53@CF) was prepared from MIL-53 (Fe) and cellulose fiber (CF) using a simple ultrasonic method for adsorption and photocatalytic degradation of organic pollutants in wastewater. The results showed MIL-53(Fe) crystals were uniformly growth on CF surfaces and bonded with surface nanofibrils of CF through physical crosslinking and hydrogen bonding.

Nanocellulose-based polyurethane foam adsorbent for pressure-driven dye-contaminated water purification.

Dye-contaminated water has caused a worldwide pollution, which is threatening aquatic organisms and human health. In this work, a pressure-driven foam adsorbent (PFA) was bioinspired from the tapestry turban for purifying the dye-contaminated water. The PFA was prepared using an one-step method from nanocellulose (NC), amino-functionalized ZIF-8 (ZIF-8-NH), and high resilience polyurethane foam (PUF).

Prediction of drug protein interactions based on variable scale characteristic pyramid convolution network.

Motivation: In the process of drug screening, it is significant to improve the accuracy of drug-target binding affinity prediction. A multilayer convolutional neural network is one of the most popular existing methods for predicting affinity based on deep learning. It uses multiple convolution layers to extract features from the simplified molecular input system (SMILES) strings of the compounds and amino acid sequences of proteins and then performs affinity prediction analysis.

Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism.

The prediction of the strengths of drug-target interactions, also called drug-target binding affinities (DTA), plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the number of drug-protein interactions, machine learning techniques, especially deep learning methods, have become applicable for drug-target interaction discovery because they significantly reduce the required experimental workload. In this paper, we present a spontaneous formulation of the DTA prediction problem as an instance of multi-instance learning.

Effect of Scanning Speed on Properties of Laser Surface Remelted 304 Stainless Steel.

In order to study the microstructure and properties of stainless steel after laser surface remelting, based on the theory of laser surface remelting, a simulation model of nanosecond-pulsed laser surface remelted stainless steel was established to study the evolution law of the Marangoni force of the molten pool during laser surface remelting. A single-lane laser remelting experiment was performed to study the variation of the scanning speed on the remelting width, roughness, and layer microtopography. The "S" scanning path was used to remelt the stainless steel surface to investigate the bonding force between the remelted layer and the substrate, the hardness, microscopic morphology, and corrosion resistance.

Peridynamic Simulation to Fracture Mechanism of CBN Grain in the Honing Wheel Dressing Process.

Regularly dressing of CBN honing wheel is an effective way to keep its sharpness and correct geometry during honing process. This study aims to understand the fracture mechanism of single CBN grain in the dressing process of honing wheel. The honing wheel dressing process was simplified into the dressing process of grinding wheel, and the bond-based Peridynamic method considering bond rotation effect was developed to investigate the progressive fracture evolution, stress characteristics, and fracture modes of CBN grains in this process.

Inverse identification of pollution source release information for surface river chemical spills using a hybrid optimization model.

To evaluate the liability of the spilled contaminant and to design comprehensive emergency response schemes, it is essential to estimate the contaminant source characteristic and identify where, when and how much the spilled contaminant is injected into a surface river. In this study, an effective pollution source inverse method is developed to reconstruct the release history of the injection location, time, and quantity, and provide appropriate emergency response schemes for dealing with surface river environmental pollution. The pollution source inverse method IGSAA is developed by an integration of genetic algorithm (IGA) and simulated annealing algorithm (SAA) in order to guarantee both the global searching ability and convergence speed.

pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived.

Complex dynamics of a delayed discrete neural network of two nonidentical neurons.

In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols.