PKCδ is required for porcine reproductive and respiratory syndrome virus replication

AbstractProtein kinase C (PKC) that transduces signals to modulate a wide range of cellular functions has been shown to regulate a number of viral infections. Herein, we showed that inhibition of PKC with the PKC inhibitor GF109203X significantly impaired porcine reproductive and respiratory syndrome virus (PRRSV) replication. Inhibition of PKC led to virus yield reduction, which was associated with decreased viral RNA synthesis and lowered virus protein expression. And this inhibitory effect by PKC inhibitor was shown to occur at the early stage of PRRSV infection. Subsequently, we found that PRRSV infection activated PKCδ in PAMs and knockdown of PKCδ by small interfering RNA (siRNA) suppressed PRRSV replication, suggesting that novel PKCδ may play an important factor in PRRSV replication. Taken together, these data imply that PKC is involved in PRRSV infection and beneficial to PRRSV replication, extending our understanding of PRRSV replication

Association of TRB3 Q84R polymorphism with polycystic ovary syndrome in Chinese women

<p>Abstract</p> <p>Background</p> <p>Tribbles 3 (TRB3) affects insulin signalling by inhibiting insulin-stimulated Akt phosphorylation and subsequent activation. A single nucleotide polymorphism located in the second extron of the human TRB3 gene is thought to be associated with insulin resistance. The latter is a core abnormality in PCOS independent of obesity. The present study was designed to clarify the relationships of TRB3 Q84R polymorphism with PCOS in a Chinese women group.</p> <p>Methods</p> <p>A case-control study with two groups: PCOS group (n = 336) and control group of infertility women for tubal and/or male factor (n = 116) was performed. Genotyping of the TRB3 R84 variant was determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP).</p> <p>Results</p> <p>The frequency of genotype QQ in PCOS women was significantly lower, while genotype QR and RR were significantly higher than that in control group (p < 0.05). However, the difference disappeared after adjustment for BMI. At glucose1h, glucose2h and insulin2h point, the difference between QQ individuals and R84 allele carriers in PCOS women reached statistical significance during OGTT (p < 0.05).</p> <p>Conclusions</p> <p>TRB3 Q84R polymorphism is associated with obesity and especially glucose metabolism and not associated with polycystic ovary syndrome because of compositional characteristics of phenotype in Chinese PCOS women.</p

Post-Quantum Public-key Authenticated Searchable Encryption with Forward Security: General Construction, Implementation, and Applications

Public-key encryption with keyword search was first proposed by Boneh et al. (EUROCRYPT 2004), achieving the ability to search for ciphertext files. Nevertheless, this scheme is vulnerable to inside keyword guessing attacks (IKGA). Public-key authenticated encryption with keyword search (PAEKS), introduced by Huang et al. (Inf. Sci. 2017), on the other hand, is secure against IKGA. Nonetheless, it is susceptible to quantum computing attacks. Liu et al. and Cheng et al. addressed this problem by reducing to the lattice hardness (AsiaCCS 2022, ESORICS 2022). Furthermore, several scholars pointed out that the threat of secret key exposure delegates a severe and realistic concern, potentially leading to privacy disclosure (EUROCRYPT 2003, Compt. J. 2022). As a result, research focusing on mitigating key exposure and resisting quantum attacks for the PAEKS primitive is significant and far-reaching. In this work, we present the first instantiation of post-quantum PAEKS primitive that is forward-secure and does not require trusted authorities, mitigating the secret key exposure while ensuring quantum-safe properties. We extended the scheme of Liu et al. (AsiaCCS 2022), and proposed a novel post-quantum PAEKS construction, namely FS-PAEKS. To begin with, we introduce the binary tree structure to represent the time periods, along with a lattice basis extension algorithm, and SamplePre algorithm to obtain the post-quantum one-way secret key evolution, allowing users to update their secret keys periodically. Furthermore, our scheme is proven to be IND-CKA, IND-IKGA, and IND-Multi-CKA in the quantum setting. In addition, we also compare the security of our primitive in terms of computational complexity and communication overhead with other top-tier schemes and provide implementation details of the ciphertext generation and test algorithms. The proposed FS-PAEKS is more efficient than the FS-PEKS scheme (IEEE TDSC 2021). Lastly, we demonstrate three potential application scenarios of FS-PAEKS

Cathepsin B Regulates Collagen Expression by Fibroblasts via Prolonging TLR2/NF- κ

Fibroblasts are essential for tissue repair due to producing collagens, and lysosomal proteinase cathepsin B (CatB) is involved in promoting chronic inflammation. We herein report that CatB regulates the expression of collagens III and IV by fibroblasts in response to a TLR2 agonist, lipopolysaccharide from Porphyromonas gingivalis (P.g. LPS). In cultured human BJ fibroblasts, mRNA expression of CatB was significantly increased, while that of collagens III and IV was significantly decreased at 24 h after challenge with P.g. LPS (1 μg/mL). The P.g. LPS-decreased collagen expression was completely inhibited by CA-074Me, the specific inhibitor of CatB. Surprisingly, expression of collagens III and IV was significantly increased in the primary fibroblasts from CatB-deficient mice after challenge with P.g. LPS. The increase of CatB was accompanied with an increase of 8-hydroxy-2′-deoxyguanosine (8-OHdG) and a decrease of IκBα. Furthermore, the P.g. LPS-increased 8-OHdG and decreased IκBα were restored by CA-074Me. Moreover, 87% of CatB and 86% of 8-OHdG were colocalized with gingival fibroblasts of chronic periodontitis patients. The findings indicate the critical role of CatB in regulating the expression of collagens III and IV by fibroblasts via prolonging TLR2/NF-κB activation and oxidative stress. CatB-specific inhibitors may therefore improve chronic inflammation-delayed tissue repair

The Professional Education Ecosystem of Industrial Design at Georgia Institute of Technology Based on SECI Model

The changes in international competitive environment and the adjustment of national industrial development strategy have put forward new requirements for the education of industrial design. Industrial design is the research on design knowledge. It includes systematic explicit knowledge and empirical tacit knowledge. The education of design is about the transfer and accumulation of this specialized knowledge. By using SECI model of knowledge transformation and creation proposed by Ikujiro Nonaka and others, this article analyzes four processes of knowledge transformation, the platform of knowledge transformation (“Ba”) and knowledge assets. It studies the educational mode of the School of Industrial Design at Georgia Institute of Technology. From basic professional education, to department-level studio courses and university-level laboratory research, to the completion of industrial design education, professional knowledge is built up through the whole process, thus a virtuous cycle of professional education ecosystem is established. It is hoped this will bring useful experience to the education of industrial design in China

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

In higher education, student learning relies increasingly on autonomy. With the rise in blended learning, both online and offline, students need to further improve their online learning effectiveness. Therefore, predicting students’ performance and identifying students who are struggling in real time to intervene is an important way to improve learning outcomes. However, currently, machine learning in grade prediction applications typically only employs a single-output prediction method and has lagging issues. To advance the prediction of time and enhance the predictive attributes, as well as address the aforementioned issues, this study proposes a multi-output hybrid ensemble model that utilizes data from the Superstar Learning Communication Platform (SLCP) to predict grades. Experimental results show that using the first six weeks of SLCP data and the Xgboost model to predict mid-term and final grades meant that accuracy reached 78.37%, which was 3–8% higher than the comparison models. Using the Gdbt model to predict homework and experiment grades, the average mean squared error was 16.76, which is better than the comparison models. This study uses a multi-output hybrid ensemble model to predict how grades can help improve student learning quality and teacher teaching effectiveness

Intelligent Control Method Research for High Rise Building Vibration by Integrating Genetic Algorithm and LSTM

In order to solve the problem of performance degradation, such as local optimality, that may occur when shallow learning is used to predict the high-rise buildings seismic response under difficult conditions, a high-rise building vibration intelligent control method integrating genetic algorithms and long short-term memory networks is proposed. First, a structural response prediction model is constructed and combined with vibration control theory. Furthermore, an intelligent control algorithm using long short-term memory networks is designed. In conjunction with this algorithm, a centralized controller that integrates convolutional neural networks at different levels is designed. The structure of the centralized control system is improved, and genetic algorithms and Lyapunov stability theory are used to optimize thenetwork hyperparameters through deep learning. The results showed that this framework had high prediction accuracy, with the smallest relative difference in predicting C-library data at &#x2212;0.0053 cm on average. The largest prediction error for B-library data was 0.015 cm on average. The long short-term memory network had the smallest prediction error and the best learning and prediction performance. When the degradation level of each layer stiffness in the benchmark model was between 10.2&#x0025; and 20.5&#x0025;, this intelligent controller achieved the best control effect, maintaining above 39.8&#x0025;. Optimized using genetic algorithm, the optimal fitness value after 80 iterations represented controllerloss function value, which were $8.3\times 10.5$ , $2.3\times 10.4$ , $2.2\times 10.4$ , and $3.0\times 10.4$ , respectively, demonstrating good prediction results. Compared with traditional trial calculation methods, this algorithm has higher computational efficiency and accuracy. The fusion of genetic algorithms and long short-term memory networks with different structural forms shows good seismic reduction effects on the time responses of benchmark models. The research method has good prediction accuracy, high reliability, and flexible system design, providing new strategies for intelligent control of high-rise building structures under different conditions

Research on user experience of smart home under the development of Internet

This paper aims to study the current situation and prospect of user experience of different smart products in smart home under the modern Internet. Through the analysis of the relationship between the user’s demand level and smart home, as well as the current situation and problems of existing products in the market, this paper studies the user demand and user experience. It is concluded that to make smart home into every user’s home, we need to fully consider the user experience in the design, and pay attention to the user’s functional needs, emotional needs and interactive experience needs. Thus, smart home becomes a meaningful home system to help users’ lives

Multi-sensory experience design analysis on elderly toy products

With the increasing needs of the people’s good life in our country, the physical and psychological needs of the elderly have also received more and more attention from society. Toy products can fill the living needs and spiritual needs of the elderly to a certain extent. Therefore, this article will be from the perspective of multi-sensory design, Case studies of different categories of elderly toy products, exploring the differences and diversity of sensory designs in elderly toy products, in order to understand the important factors that need attention in the design of toy products for the elderly, upgrade the elements of the development of toy products for the elderly

Research on Design Strategies of Cognitive Training System Based on SCD Elderly Behavioral and Psychological Characteristics

Alzheimer’s disease (hereinafter referred to as AD) has become a major global health problem for the elderly. Combining with the current computer-aided design system design for prevention and training of Alzheimer’s disease is one of the important ways to solve this problem. This study aims at the early subjective cognitive decline of Alzheimer’s disease (hereinafter referred to as SCD) . Through investigation and research, the behavior and psychological characteristics of SCD elderly are sorted out, and the main functions of cognitive training system are located. The main functions, interactions, colors and layout of the current cognitive system are summarized by analyzing the existing cognitive training system, and compared with the research results, a more reasonable system function, interaction mode, interface color and layout are determined. Therefore, the cognitive training system design strategy based on the behavioral and psychological characteristics of SCD elderly is summarized