Whole-cell biocatalysis for ε-poly-l-lysine production by a food-grade recombinant Bacillus subtilis.

ε-Poly-l-lysine (ε-PL), a natural food preservative with various advantages, is primarily produced by Streptomyces. It has attracted considerable attentions for the outstanding antibacterial activity, safety, heat stability, water solubility and other remarkable properties. In this study, a food-grade recombinant Bacillus subtilis was constructed for the biocatalysis of ε-PL. Firstly, the d-alanine racemase gene (alrA) was deleted from the genome of Bacillus subtilis 168 to construct an auxotrophic B. subtilis 168 (alrA-). Based on the shuttle plasmid pMA5, a food-grade plasmid pMA5a was constructed by replacing the genes of kanamycin resistance (Kanr) and ampicillin resistance (Ampr) with alrA and the gene encoding α-peptide of ß-galactosidase (lacZα), respectively. Subsequently, codon-optimized ε-PL synthase gene (pls) and P-pls were ligated into pMA5a and transformed in E. coli DH5α and expressed in B. subtilis 168 (alrA-). Finally, the whole-cell biocatalysis conditions for ε-PL production by B. subtilis 168 (alrA-)/pMA5a-pls were optimized, and the optimal conditions were 30°C, pH 4, l-lysine concentration of 0.6 g/L, bacterial concentration of 15 % (w/v) and a catalytic time of 7 h. The ε-PL production reached a maximum of 0.33 ± 0.03 g/L. The product was verified to be ε-PL by HPLC and tricine-SDS-PAGE. The information obtained in this study shows critical reference for the food-grade heterologous expression of ε-PL.

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks.

The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.

Chinese text dual attention network for aspect-level sentiment classification.

English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text often conveys information using situational settings, which results in loose sentence structures, and even most Chinese comments and experimental summary texts lack subjects. This makes it challenging to determine the dependency relationship between words in Chinese text, especially in aspect-level sentiment recognition. To solve this problem faced by Chinese text in the field of sentiment recognition, a Chinese text dual attention network for aspect-level sentiment recognition is proposed. First, Chinese syntactic dependency is proposed, and sentiment dictionary is introduced to quickly and accurately extract aspect-level sentiment words, opinion extraction and classification of sentimental trends in text. Additionally, in order to extract context-level features, the CNN-BILSTM model and position coding are also introduced. Finally, to better extract fine-grained aspect-level sentiment, a two-level attention mechanism is used. Compared with ten advanced baseline models, the model's capabilities are being further optimized for better performance, with Accuracy of 0.9180, 0.9080 and 0.8380 respectively. This method is being demonstrated by a vast array of experiments to achieve higher performance in aspect-level sentiment recognition in less time, and ablation experiments demonstrate the importance of each module of the model.

Structure-based discovery of novel α-aminoketone derivatives as dual p53-MDM2/MDMX inhibitors for the treatment of cancer.

The function of the p53 protein is impaired by the overexpression of its negative regulator murine double minute 2 protein (MDM2) and homologous protein MDMX. Disruption of the p53-MDM2/MDMX interaction to restore the transcriptional function of p53 is considered a promising strategy for cancer therapy. To design dual MDM2/MDMX inhibitors, the binding modes of MDM2 or MDMX with their inhibitors are elucidated. Several hot-spot residues of MDM2 or MDMX are identified by molecular dynamics simulations, alanine scanning and MM-GBSA calculations. Then, focusing on the interaction with hot-spot residues, two series of derivatives bearing 1,3-diketone and α-aminoketone scaffolds are designed and synthesized. Among these compounds, C16 is identified as the most potent compound with low micromolar binding affinities with MDM2 and MDMX. C16 also displays moderate antiproliferative activities against MDM2-overexpressing and MDMX-overexpressing cells, with IC50 values of 0.68 µM in HCT116 cells and 0.54 µM in SH-SY5Y cells. Furthermore, C16 inhibits cell migration and invasion, reactivates the function of p53, arrests the cell cycle and induces cellular apoptosis in HCT116 and SH-SY5Y cells. Collectively, C16 can be developed as a dual MDM2 and MDMX inhibitor for cancer therapy.

Domain generation algorithms detection with feature extraction and Domain Center construction.

Network attacks using Command and Control (C&C) servers have increased significantly. To hide their C&C servers, attackers often use Domain Generation Algorithms (DGA), which automatically generate domain names for C&C servers. Researchers have constructed many unique feature sets and detected DGA domains through machine learning or deep learning models. However, due to the limited features contained in the domain name, the DGA detection results are limited. In order to overcome this problem, the domain name features, the Whois features and the N-gram features are extracted for DGA detection. To obtain the N-gram features, the domain name whitelist and blacklist substring feature sets are constructed. In addition, a deep learning model based on BiLSTM, Attention and CNN is constructed. Additionally, the Domain Center is constructed for fast classification of domain names. Multiple comparative experiment results prove that the proposed model not only gets the best Accuracy, Precision, Recall and F1, but also greatly reduces the detection time.

Identification of 9H-purin-6-amine derivatives as novel aldose reductase inhibitors for the treatment of diabetic complications.

A series of 9H-purin-6-amine derivatives as aldose reductase (ALR) inhibitors were designed and synthesized. Most of these derivatives, having a C6-substituted benzylamine side chain and N9 carboxylic acid on the core structure, were found to be potent and selective ALR inhibitors, with submicromolar IC50 values against ALR2. Particularly, compound 4e was the most active with an IC50 value of 0.038 µM, and it was also proved to be endowed with excellent inhibitory selectivity. The structure-activity relationship and molecular docking studies highlighted the importance of the carboxylic acid head group along with different halogen substituents on the C6 benzylamine side chain of the 9H-purin-6-amine scaffold for the construction of strong and selective ALR inhibitors.

Dichroic Circular Polarizers Based on Plasmonics for Polarization Imaging Applications.

Dichroic circular polarizers (DCP) represent an important group of optical filters that transfer only that part of the incident light with the desired polarization state and absorb the remainder. However, DCPs are usually bulky and exhibit significant optical loss. Moreover, the integration of these kinds of DCP devices can be difficult and costly as different compositions of chemicals are needed to achieve the desired polarization status. Circular polarizers based on metasurfaces require only thin films in the order of hundreds of nanometers but are limited by their sensitivity to angle of incidence. Furthermore, few existing solutions offer broadband operation in the visible range. By using computational simulations, this paper proposes and analyses a plasmonic DCP structure operating in the visible, from 400 nm to 700 nm which overcomes these drawbacks. The resulting circular dichroism transmission (CDT) is more than 0.9, and the maximum transmission efficiency is greater than 78% at visible wavelengths. These CDT characteristics are largely independent of angle of incidence up to angles of 80 degrees.

Salting-out extraction of ginsenosides from the enzymatic hydrolysates of Panax quinquefolium based on ethanol/sodium carbonate system.

BACKGROUND: Salting-out extraction (SOE) had been developed as a special branch of aqueous two-phase system recently. So far as we know, few reports involved in extracting ginsenosides with SOE because of the lower recovery caused by the unique solubility and surface activity of ginsenosides. A new SOE method for rapid pretreatment of ginsenosides from the enzymatic hydrolysates of Panax quinquefolium was established in this article. METHODS: The SOE system comprising ethanol and sodium carbonate was selected to extract ginsenosides from the enzymatic hydrolysates of Panax quinquefolium, and HPLC was applied to analyze the ginsenosides. RESULTS: The optimized extraction conditions were as follows: the aqueous two-phase extraction system comprising ethanol, sodium carbonate, ethanol concentration of 41.51%, and the mass percent of sodium carbonate of 7.9% in the extraction system under the experimental condition. Extraction time had minor influence on extraction efficiency of ginsenosides. The results also showed that the extraction efficiencies of three ginsenosides were all more than 90.0% only in a single step. CONCLUSION: The proposed method had been successfully applied to determine ginsenosides in enzymatic hydrolysate and demonstrated as a powerful technique for separating and purifying ginsenosides in complex samples.

Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing.

Histone deacetylases (HDACs), a critical family of epigenetic enzymes, has emerged as a promising target for antitumor drugs. Here, we describe our protocol of virtual screening in identification of novel potential HDAC inhibitors through pharmacophore modeling, 3D-QSAR, molecular docking and molecular dynamics (MD) simulation. Considering the limitation of current virtual screening works, drug repurposing strategy was applied to discover druggable HDAC inhibitor. The ligand-based pharmacophore and 3D-QSAR models were established, and their reliability was validated by different methods. Then, the DrugBank database was screened, followed by molecular docking. MD simulation (100 ns) was performed to further study the stability of ligand binding modes. Finally, results indicated the hit DB03889 with high in silico inhibitory potency was suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

Discovery of novel serine/threonine protein phosphatase 1 inhibitors from traditional Chinese medicine through virtual screening and biological assays.

Protein phosphatase 1 (PP1) is a critical regulator of several processes, such as muscle contraction, neuronal signaling, glycogen synthesis, and cell proliferation. Dysregulation of PP1 has recently been found to be implicated in cardiac dysfunctions, which indicates that PP1 could be an attractive therapeutic target. However, discovery of PP1 inhibitors with satisfied safety and efficiency is still a challenge. Here, in order to discover potential PP1 inhibitors, compounds extracted from traditional Chinese medicine (TCM) were screened by a novel integrated virtual screening protocol including pharmacophore modeling and docking approaches. Combined with protein phosphatase inhibition assay, ZINC43060554 showed strongly inhibitory activity with IC50 values of 26.78 µM. Furthermore, molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area binding free-energy analysis were performed to examine the stability of ligand binding modes. These novel scaffolds discovered in the present study can be used for rational design of PP1 inhibitors with high affinity.Communicated by Ramaswamy H. Sarma.

A New Microextraction Technique for the Assay of Alkaloids in Chinese Compound Formula-Based Polyether Sulfone Membrane Fiber Decorated by TiO2 Nanoparticles.

A new nanocomposite membrane was used to clean up impurities from complex samples and the obvious synergy was obtained in this paper. The nanocomposite membrane was prepared by dispersing TiO2 nanoparticles in chloroform and filled in the pores and lumen of polyether sulfone membrane fiber. The novel microextraction method showed the ideal selective extraction effect for alkaloids in the formulae composed of Rhizoma coptidis and the excellent clean-up efficiency compared with the single membrane method. The optimum extraction conditions were as follows: chloroform as accepted phase; the number of nanocomposite membrane fiber bars, 7; extraction time, 30 min; pH of the sample solution, 10.55; desorption solvent, methanol. The limit of detection for the described alkaloids was estimated at 0.122 µg mL-1. The recovery of the four alkaloids in complex samples ranged from 93.24% to 97.94% with relative standard deviation of <4.99 (n = 5). The validated method had been successfully applied to study the transfer rate of alkaloids in the producing process of Qihuang capsule and the ideal transfer rate of alkaloids was obtained in this paper.