Prokaryotic expression, purification and immunogenicity analysis of CpsD protein from Streptococcus iniae

Streptococcus iniae is a major cause of serious bacterial infections in both fish and human beings. Capsular polysaccharide (CPS) of S. iniae is vital to evade phagocytic clearance of the host and serves as an important protective antigen of S. iniae infection in aquatic animals. The CpsD gene was determined to be highly conservative in capsule polysaccharide operon. Prokaryotic expression of the CpsD gene of a clinical isolate of S. iniae from channel catfish and immunogenic examination of the recombinant protein were first described in this essay. The recombinant protein was expressed in the form of inclusion bodies (IBs). Induction conditions in Escherichia coli were optimized with 0.6mM Isopropyl β-D-1-Thiogalactopyranoside at 37°C for 5h after the culture mid-log phase in Luria Bertani (LB) medium. The recombinant protein CpsD was thus expressed and purified by immobilized metal affinity chromatography (IMAC), yielding approximate 582.47 mg the protein per liter culture. Western blot analysis showed that the purified CpsD had reactogenicity. It will possibly reveal more details of capsule synthesis and capsule regulation during various stages of the S. iniae infectious process

Gene transcription analysis during interaction between potato and Ralstonia solanacearum

Bacterial wilt (BW) caused by Ralstonia solanacearum (Rs) is an important quarantine disease that spreads worldwide and infects hundreds of plant species. The BW defense response of potato is a complicated continuous process, which involves transcription of a battery of genes. The molecular mechanisms of potato-Rs interactions are poorly understood. In this study, we combined suppression subtractive hybridization and macroarray hybridization to identify genes that are differentially expressed during the incompatible interaction between Rs and potato. In total, 302 differentially expressed genes were identified and classified into 12 groups according to their putative biological functions. Of 302 genes, 81 were considered as Rs resistance-related genes based on the homology to genes of known function, and they have putative roles in pathogen recognition, signal transduction, transcription factor functioning, hypersensitive response, systemic acquired resistance, and cell rescue and protection. Additionally, 50 out of 302 genes had no match or low similarity in the NCBI databases, and they may represent novel genes. Of seven interesting genes analyzed via RNA gel blot and semi-quantitative RT-PCR, six were induced, one was suppressed, and all had different transcription patterns. The results demonstrate that the response of potato against Rs is rapid and involves the induction of numerous various genes. The genes identified in this study add to our knowledge of potato resistance to Rs

“PM！I want to sign up”: Decoding Parental Decision-making in Educational S-commerce Community

This study delves into the prevalence and impact of private supplementary tutoring in Hong Kong, where a significant percentage of Secondary Three and Secondary Six students seek such services. The proliferation of tutoring and interest class advertisements across various media platforms underscores the educational landscape\u27s emphasis on private tutoring. The emergence of educational social commerce communities on social media platforms has further facilitated the exchange of educational information, interest activities, and parenting tips. By analyzing factors influencing parent engagement in these communities, such as perceived ease-of-use, perceived usefulness, sociability, and informativeness, this research sheds light on the dynamics of educational s-commerce. Additionally, the study explores how post characteristics and scarcity of educational resources influence parent engagement and recommendation willingness within these communities. Understanding these factors is crucial for enhancing customer engagement and optimizing the educational social commerce experience

Domain Wall Resistance in Perpendicular (Ga,Mn)As: dependence on pinning

We have investigated the domain wall resistance for two types of domain walls in a (Ga,Mn)As Hall bar with perpendicular magnetization. A sizeable positive intrinsic DWR is inferred for domain walls that are pinned at an etching step, which is quite consistent with earlier observations. However, much lower intrinsic domain wall resistance is obtained when domain walls are formed by pinning lines in unetched material. This indicates that the spin transport across a domain wall is strongly influenced by the nature of the pinning.Comment: 9 pages, 3 figure

Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process

Increasing demand for flexible operation has posed significant challenges to the control system design of solvent-based post-combustion CO2 capture (PCC) process: 1) the capture system itself has very slow dynamics; 2) in the case of wide range of operation, dynamic behavior of the PCC process will change significantly at different operating points; and 3) the frequent variation of upstream flue gas flowrate will bring in strong disturbances to the capture system. For these reasons, this paper provides a comprehensive study on the dynamic characteristics of the PCC process. The system dynamics under different CO2 capture rates, re-boiler temperatures, and flue gas flow rates are analyzed and compared through step-response tests. Based on the in-depth understanding of the system behavior, a disturbance rejection predictive controller (DRPC) is proposed for the PCC process. The predictive controller can track the desired CO2 capture rate quickly and smoothly in a wide operating range while tightly maintaining the re-boiler temperature around the optimal value. Active disturbance rejection approach is used in the predictive control design to improve the control property in the presence of dynamic variations or disturbances. The measured disturbances, such as the flue gas flow rate, is considered as an additional input in the predictive model development, so that accurate model prediction and timely control adjustment can be made once the disturbance is detected. For unmeasured disturbances, including model mismatches, plant behavior variations, etc., a disturbance observer is designed to estimate the value of disturbances. The estimated signal is then used as a compensation to the predictive control signal to remove the influence of disturbances. Simulations on a monoethanolamine (MEA) based PCC system developed on gCCS demonstrates the excellent effect of the proposed controller

Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls

Solvent-based post-combustion CO2 capture (PCC) provides a promising technology for the CO2 removal of coal-fired power plant (CFPP). However, there are strong interactions between the CFPP and the PCC system, which makes it challenging to attain a good control for the integrated plant. The PCC system requires extraction of large amounts of steam from the intermediate/low pressure steam turbine to provide heat for solvent regeneration, which will reduce power generation. Wide-range load variation of power plant will cause strong fluctuation of the flue gas flow and brings in a significant impact on the PCC system. To overcome these issues, this paper presents a reinforced coordinated control scheme for the integrated CFPP-PCC system based on the investigation of the overall plant dynamic behavior. Two model predictive controllers are developed for the CFPP and PCC plants respectively, in which the steam flow rate to re-boiler and the flue-gas flow rate are considered as feed-forward signals to link the two systems together. Three operating modes are considered for designing the coordinated control system, which are: (1) normal operating mode; (2) rapid power load change mode; and (3) strict carbon capture mode. The proposed coordinated controller can enhance the overall performance of the CFPP-PCC plant and achieve a flexible trade-off between power generation and CO2 reduction. Simulation results on a small-scale subcritical CFPP-PCC plant developed on gCCS demonstrates the effectiveness of the proposed controller

Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control

This paper presents a controller design study for the supercritical coal fired power plant (CFPP) integrated with solvent-based post-combustion CO2 capture (PCC) system. The focus of the study is on the steam drawn-off from turbine to the re-boiler, which is the key interaction between the CFPP and PCC plants. The simulation study of a 660 MW supercritical CFPP-PCC unit model has shown that the impact of re-boiler steam change on the power generation of CFPP is more than 100 times faster than that on the PCC operation. Considering this finding, a collaborative predictive control strategy is proposed for the CFPP-PCC system where the re-boiler steam flowrate is manipulated for the CFPP load ramping and then gradually set to the required value for CO2 capture. The PCC is thereby exploited as an energy storage device, which can quickly store/release extra energy for the CFPP in addition to the primary function of carbon emission reduction. The simulation results show that the proposed collaborative predictive controller can effectively improve the load ramping performance of CFPP without much performance degradation on the PCC operation

High quality GaMnAs films grown with As dimers

We demonstrate that GaMnAs films grown with As2 have excellent structural, electrical and magnetic properties, comparable or better than similar films grown with As4. Using As2, a Curie temperature of 112K has been achieved, which is slightly higher than the best reported to date. More significantly, films showing metallic conduction have been obtained over a much wider range of Mn concentrations (from 1.5% to 8%) than has been reported for films grown with As4. The improved properties of the films grown with As2 are related to the lower concentration of antisite defects at the low growth temperatures employed.Comment: 8 pages, accepted for publication in J. Crystal Growt

Variational approximation for mixtures of linear mixed models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG