c-FLIP Degradation Mediates Sensitization of Pancreatic Cancer Cells to TRAIL-Induced Apoptosis by the Histone Deacetylase Inhibitor LBH589

Great efforts have been made to develop novel and efficacious therapeutics against pancreatic cancer to improve the treatment outcomes. Tumor-necrosis factor-related apoptosis-inducing ligand (TRAIL) is such a therapeutic cytokine with selective killing effect toward malignant cells. However, some human pancreatic cancers are intrinsically resistant to TRAIL-mediated apoptosis or therapy. In this study, we have shown that the histone deacetylase inhibitor LBH589 can synergize with TRAIL to augment apoptosis even in TRAIL-resistant cells. LBH589 decreased c-FLIP levels in every tested cell line and survivin levels in some of the tested cell lines. Enforced expression of ectopic c-FLIP, but not survivin, abolished the cooperative induction of apoptosis by the combination of LBH589 and TRAIL, indicating that c-FLIP downregulation plays a critical role in LBH589 sensitization of pancreatic cancer cells to TRAIL. Moreover, LBH589 decreased c-FLIP stability and the presence of the proteasome inhibitor MG132 prevented c-FLIP from reduction by LBH589. Correspondingly, we detected increased levels of ubiqutinated c-FLIP in LBH589-treated cells. These data thus indicate that LBH589 promotes ubiqutin/proteasome-mediated degradation of c-FLIP, leading to downregulation of c-FLIP. Collectively, LBH589 induces c-FLIP degradation and accordingly sensitizes pancreatic cancer cells to TRAIL-induced apoptosis, highlighting a novel therapeutic regimen against pancreatic cancer

Capsaicin Protects Mice from Community-Associated Methicillin-Resistant Staphylococcus aureus Pneumonia

BACKGROUND: α-toxin is one of the major virulence factors secreted by most Staphylococcus aureus strains, which played a central role in the pathogenesis of S. aureus pneumonia. The aim of this study was to investigate the impact of capsaicin on the production of α-toxin by community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) strain USA 300 and to further assess its performance in the treatment of CA-MRSA pneumonia in a mouse model. METHODOLOGY/PRINCIPAL FINDINGS: The in vitro effects of capsaicin on α-toxin production by S. aureus USA 300 were determined using hemolysis, western blot, and real-time RT-PCR assays. The influence of capsaicin on the α-toxin-mediated injury of human alveolar epithelial cells was determined using viability and cytotoxicity assays. Mice were infected intranasally with S. aureus USA300; the in vivo protective effects of capsaicin against S. aureus pneumonia were assessed by monitoring the mortality, histopathological changes and cytokine levels. Low concentrations of capsaicin substantially decreased the production of α-toxin by S. aureus USA 300 without affecting the bacterial viability. The addition of capsaicin prevented α-toxin-mediated human alveolar cell (A549) injury in co-culture with S. aureus. Furthermore, the in vivo experiments indicated that capsaicin protected mice from CA-MRSA pneumonia caused by strain USA 300. CONCLUSIONS/SIGNIFICANCE: Capsaicin inhibits the production of α-toxin by CA-MRSA strain USA 300 in vitro and protects mice from CA-MRSA pneumonia in vivo. However, the results need further confirmation with other CA-MRSA lineages. This study supports the views of anti-virulence as a new antibacterial approach for chemotherapy

Optimization of Enhanced Geothermal System Power Generation Investment Scheme Based on Fuzzy VIKOR Model

In the future, Enhanced Geothermal System (EGS) power generation will be a hot spot for new energy investment development. How to optimize the EGS power generation investment program is an important issue to be solved by the government and enterprises. In order to improve the accuracy of the EGS investment project evaluation, this paper proposes a technical route to optimize the EGS power generation investment program. It comprehensively considers the ambiguity of the decision attributes and the conflict between the indicators. The index is calculated using the entropy method and the order relationship method. Comprehensive weights; using triangular fuzzy numbers instead of exact numbers to reduce the loss of decision information; using a multi-criteria compromised sorting method to obtain trade-off solutions to solve the problem that investment plans cannot be accepted by all decision makers when there are multiple conflicting indicators. Finally, taking an enterprise’s EGS power generation project as an example, the effectiveness and rationality of the optimized technology route for the EGS power generation investment program are verified

Optimization of Enhanced Geothermal System Power Generation Investment Scheme Based on Fuzzy VIKOR Model

In the future, Enhanced Geothermal System (EGS) power generation will be a hot spot for new energy investment development. How to optimize the EGS power generation investment program is an important issue to be solved by the government and enterprises. In order to improve the accuracy of the EGS investment project evaluation, this paper proposes a technical route to optimize the EGS power generation investment program. It comprehensively considers the ambiguity of the decision attributes and the conflict between the indicators. The index is calculated using the entropy method and the order relationship method. Comprehensive weights; using triangular fuzzy numbers instead of exact numbers to reduce the loss of decision information; using a multi-criteria compromised sorting method to obtain trade-off solutions to solve the problem that investment plans cannot be accepted by all decision makers when there are multiple conflicting indicators. Finally, taking an enterprise’s EGS power generation project as an example, the effectiveness and rationality of the optimized technology route for the EGS power generation investment program are verified

Searching for protein variants with desired properties using deep generative models

Abstract Background Protein engineering aims to improve the functional properties of existing proteins to meet people’s needs. Current deep learning-based models have captured evolutionary, functional, and biochemical features contained in amino acid sequences. However, the existing generative models need to be improved when capturing the relationship between amino acid sites on longer sequences. At the same time, the distribution of protein sequences in the homologous family has a specific positional relationship in the latent space. We want to use this relationship to search for new variants directly from the vicinity of better-performing varieties. Results To improve the representation learning ability of the model for longer sequences and the similarity between the generated sequences and the original sequences, we propose a temporal variational autoencoder (T-VAE) model. T-VAE consists of an encoder and a decoder. The encoder expands the receptive field of neurons in the network structure by dilated causal convolution, thereby improving the encoding representation ability of longer sequences. The decoder decodes the sampled data into variants closely resembling the original sequence. Conclusion Compared to other models, the person correlation coefficient between the predicted values of protein fitness obtained by T-VAE and the truth values was higher, and the mean absolute deviation was lower. In addition, the T-VAE model has a better representation learning ability for longer sequences when comparing the encoding of protein sequences of different lengths. These results show that our model has more advantages in representation learning for longer sequences. To verify the model’s generative effect, we also calculate the sequence identity between the generated data and the input data. The sequence identity obtained by T-VAE improved by 12.9% compared to the baseline model

Analysis and Discussion about Quantitative Grading Standard for Salary Promotion of Agricultural Scientific Researchers

The existing performance salary system has several grades in the same job title. When the number of workers qualified for promotion is more than the target, quantitative grading is usually adopted to determine salary promotion personnel. Scientific, fair and reasonable grading content and standard directly concern income and remuneration of scientific researchers, and also concern recognition and respect degree of contribution made by scientific researchers. Therefore, quantitative grading standard is of the utmost importance to keeping stability, arousing enthusiasm and creativity of scientific researchers, and promoting smooth development of scientific research. Achievements awarded, papers published and project research can reflect scientific research level, ability and working performance of agricultural scientific researchers. This paper takes these three items as examples, analyzes, discusses and compares the establishment and evaluation of quantitative grading standard. It states that “one yardstick” and “one vote veto system” should be adhered to when evaluating using the quantitative grading standard. It is expected to provide reference for organizations of the same trade in establishing quantitative grading standard and conducting evaluation

Natural Gas Security in China: A Simulation of Evolutionary Trajectory and Obstacle Degree Analysis

Studying the security status of China’s natural gas supply and proposing a feasible coping strategy for enhancing that security is of great significance. We use a pressure-state-response (PSR) analysis framework and the exponential weighting method to make a systematic evaluation of China’s natural gas security, predict the evolution of natural gas security combined with the GM(1,1) model, and use the obstacle degree model to diagnose the obstacles standing in the way of China’s optimum natural gas security. China’s natural gas comprehensive security index from 2006 to 2015 was between 0.627 and 0.740, and it is predicted to land between 0.669 and 0.759 from 2016 to 2025. The barriers affecting China’s natural gas security moving forward will be focused on urban development pressure, natural gas consumption growth pressure, supply-demand ratio, storage-production ratio, import price volatility for liquefied natural gas, and import dependence. We predict China’s natural gas security will be characterized by a wave of advancement, and has certain periodicity. The main internal factors affecting China’s natural gas security will shift from the rudimentary natural gas pipeline construction and gas storage facilities construction to the low availability of natural gas and urbanization, which will increase the pressure on natural gas supply and demand

Modeling shear behavior and strain localization in cemented sands by two-dimensional distinct element method analyses

This paper presents a numerical investigation of shear behavior and strain localization in cemented sands using the distinct element method (DEM), employing two different failure criteria for grain bonding. The first criterion is characterized by a Mohr–Coulomb failure line with two distinctive contributions, cohesive and frictional, which sum to give the total bond resistance; the second features a constant, pressure-independent strength at low compressive forces and purely frictional resistance at high forces, which is the standard bond model implemented in the Particle Flow Code (PFC2D). Dilatancy, material friction angle and cohesion, strain and stress fields, the distribution of bond breakages, the void ratio and the averaged pure rotation rate (APR) were examined to elucidate the relations between micromechanical variables and macromechanical responses in DEM specimens subjected to biaxial compression tests. A good agreement was found between the predictions of the numerical analyses and the available experimental results in terms of macromechanical responses. In addition, with the onset of shear banding, inhomogeneous fields of void ratio, bond breakage and APR emerged in the numerical specimens