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To explore the design of the English pronunciation quality evaluation system, a design of the English pronunciation quality evaluation system based on a deep learning model is proposed. This method explores the research of English pronunciation quality evaluation by recommending key technical problems and solutions based on information represented by the deep learning model. The research shows that the efficiency of the English pronunciation quality evaluation system based on a deep learning model is about 30% higher than that of traditional methods. Through the experimental verification, the English pronunciation quality evaluation model method is reasonable and reliable. It can give learners timely, accurate, and objective evaluation and feedback guidance, help learners find out the difference between their pronunciation and standard pronunciation, correct pronunciation errors, and improve the efficiency of English spoken language learning.

Cisplatin and other platinum-based compounds are frequently used to treat breast cancer, but their utility is severely compromised by drug resistance. Many genes dictating drug responsiveness are subject to pre-mRNA alternative splicing which is regulated by key kinases such as the serine-arginine protein kinase 1 (SRPK1). However, its contribution to drug resistance remains controversial. In this study, we have identified that Tip60-mediated acetylation of SRPK1 is closely associated with chemotherapy sensitivity. In breast cancer cells, cisplatin induced SRPK1 acetylation but in the corresponding resistant cells, it reduced acetylation yet increased phosphorylation and kinase activity of SRPK1, favouring the splicing of some anti-apoptotic variants. Significantly, the cisplatin-resistant cells could be re-sensitized by enhancing SRPK1 acetylation or inhibiting its kinase activity. Hence, our study reveals a key role of SRPK1 in the development of cisplatin resistance in breast cancer cells and suggests a potential therapeutic avenue for overcoming chemotherapy resistance.

More and more electronic devices are integrated into the modern vehicles with the development of intelligent vehicles. There are various design flaws and vulnerabilities hidden in a large number of hardware, firmware and software. Therefore, the vulnerabilities of intelligent vehicles have become the most important factor affecting the vehicle safety. The safety of vehicles is seriously affected by the disclosure of a large number of vulnerabilities, and the wide application of smart cars is also restricted. Vulnerability management is an effective method to reduce the risk of vulnerabilities and improve vehicle security. And vulnerability scoring is one the important step in vulnerability management procedure. However, current method have no capability assessing automotive vulnerabilities reasonably. In order to handle this problem, a vulnerability scoring model for intelligent vehicles was proposed, which was based on CVSS. The attack vector and attack complexity were optimized, and property security, privacy security, functional safety and life safety were added to characterize the possible impact of the vulnerabilities according to the characteristics of intelligent vehicles. With the machine learning method, the parameters in CVSS scoring formula were optimized to describe the characteristics of intelligent vehicle vulnerabilities and adapt to the adjusted and new added weights. It is found in case study and statistics that the diversity and distribution of the model are better than CVSS, which means the model can better score different vulnerabilities. And then AHP is used to evaluate the vulnerability of the whole vehicle based on the vulnerability score of the model, a score is given representing the risk level of whole vehicle. The proposed model can be used to evaluate the severity of information security vulnerabilities in intelligent vehicles and assess the security risks of the entire vehicle or part of the system reasonably, which can provide an evidence for fixing the vulnerabilities or reinforcing the entire vehicle.

Glycyl-tRNA synthetase (GlyRS) is the enzyme that covalently links glycine to cognate tRNA for translation. It is of great research interest because of its nonconserved quaternary structures, unique species-specific aminoacylation properties, and noncanonical functions in neurological diseases, but none of these is fully understood. We report two crystal structures of human GlyRS variants, in the free form and in complex with tRNA(Gly) respectively, and reveal new aspects of the glycylation mechanism. We discover that insertion 3 differs considerably in conformation in catalysis and that it acts like a "switch" and fully opens to allow tRNA to bind in a cross-subunit fashion. The flexibility of the protein is supported by molecular dynamics simulation, as well as enzymatic activity assays. The biophysical and biochemical studies suggest that human GlyRS may utilize its flexibility for both the traditional function (regulate tRNA binding) and alternative functions (roles in diseases).

QPQ treating solves rusty problems for concrete pump truck boom connecting pins. Analysis and comparison are implemented in the pins’ fatigue lives with QPQ and ordinary thermal refining. Method feasibility is verified,fatigue reliability being the theoretical basis. Maximum bending moment helps calculate the allowable fatigue load. Test-tooling simulates the assembly structures and actual working conditions. The results show that QPQ treatment slightly influences the surface hardness and fatigue life. References and bases are provided for the design calculation and surface treatment. QPQ treatment pins are trialproduced and they do well in some concrete pump trucks.

Akt activation is a hallmark of human cancers. Here, we report a critical mechanism for regulation of Akt activity by the splicing kinase SRPK1, a downstream Akt target for transducing growth signals to regulate splicing. Surprisingly, we find that SRPK1 has a tumor suppressor function because ablation of SRPK1 in mouse embryonic fibroblasts induces cell transformation. We link the phenotype to constitutive Akt activation from genome-wide phosphoproteomics analysis and discover that downregulated SRPK1 impairs the recruitment of the Akt phosphatase PHLPP1 (pleckstrin homology (PH) domain leucine-rich repeat protein phosphatase) to Akt. Interestingly, SRPK1 overexpression is also tumorigenic because excess SRPK1 squelches PHLPP1. Thus, aberrant SRPK1 expression in either direction induces constitutive Akt activation, providing a mechanistic basis for previous observations that SRPK1 is downregulated in some cancer contexts and upregulated in others.

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