A New Continuous Flow Microwave Radiation Process Design for Non-Isocyanate Polyurethane (NIPU).

Non-isocyanate Polyurethane (NIPU) has been known to result from a thermal-ring-opening reaction between bis-cyclic carbonate (BCC) compounds and polyamines. BCC can be obtained from carbon dioxide capture using an epoxidized compound. Microwave radiation has been found to be an alternative process to conventional heating for synthesizing NIPU on a laboratory scale. The microwave radiation process is far more efficient (>1000 times faster) than using a conventional heating reactor. Now, a flow tube reactor has been designed for a continuous and recirculating microwave radiation system for scaling up NIPU. Furthermore, the TOE (Turn Over Energy) of the microwave for a lab batch (24.61 g) reactor was 24.38 kJ/g. This decreased to 8.89 kJ/g with an increase in reaction size of up to 300 times with this new continuous microwave radiation system. This proves that synthesizing NIPU with this newly-designed continuous and recirculating microwave radiation process is not only a reliable energy-saving method, but is also convenient for scale-up, making it a green process.

Design and Application of a Variable Selection Method for Multilayer Perceptron Neural Network With LASSO.

In this paper, a novel variable selection method for neural network that can be applied to describe nonlinear industrial processes is developed. The proposed method is an iterative two-step approach. First, a multilayer perceptron is constructed. Second, the least absolute shrinkage and selection operator is introduced to select the input variables that are truly essential to the model with the shrinkage parameter is determined using a cross-validation method. Then, variables whose input weights are zero are eliminated from the data set. The algorithm is repeated until there is no improvement in the model accuracy. Simulation examples as well as an industrial application in a crude distillation unit are used to validate the proposed algorithm. The results show that the proposed approach can be used to construct a more compressed model, which incorporates a higher level of prediction accuracy than other existing methods.

Layered protonated titanate nanosheets synthesized with a simple one-step, low-temperature, urea-modulated method as an effective pollutant adsorbent.

A simple one-step, low-temperature, urea-modulated method is developed for the synthesis of layered protonated titanate nanosheets (LPTNs). Urea serves as an indirect ammonium ion source, and the controlled supply of the ammonium ion slows the crystalline formation process and enables the production of the LPTNs from amorphous intermediate through aging-induced restructuring. The resulting LPTNs exhibit excellent adsorption capacities for methylene blue and Pb(2+) because of their high specific surface areas and excellent ion-exchange capability. Intercalation of Pb(2+) into the interlayer space of the LPTNs is evidenced by the relevant X-ray diffraction patterns on perturbation of the layered structure. The LPTNs prove to be a promising adsorbent in wastewater treatment for adsorption removal of metal ions or cationic organic dyes.

Takagi-Sugeno model based analysis of EWMA RtR control of batch processes with stochastic metrology delay and mixed products.

In many batch-based industrial manufacturing processes, feedback run-to-run control is used to improve production quality. However, measurements may be expensive and cannot always be performed online. Thus, the measurement delay always exists. The metrology delay will affect the stability and performance of the process. Moreover, since quality measurements are performed offline, delay is not fixed but is stochastic in nature. In this paper, a modeling approach Takagi-Sugeno (T-S) model is presented to handle stochastic metrology delay in both single-product and mixed-product processes. Based on the Markov characteristics of the delay, the membership of the T-S model is derived. Performance indices such as the mean and the variance of the closed-loop output of the exponentially weighted moving average (EWMA) control algorithm can be derived. A steady-state error of the process output always exists, which leads the output deviating from the target. To remove the steady-state error, an algorithm called compensatory EWMA run-to-run (COM-EWMA-RtR) algorithm is proposed. The validity of the T-S model analysis and the efficiency of the proposed COM-EWMA-RtR algorithm are confirmed by simulation.

Dynamic transcript profiling of Candida albicans infection in zebrafish: a pathogen-host interaction study.

Candida albicans is responsible for a number of life-threatening infections and causes considerable morbidity and mortality in immunocompromised patients. Previous studies of C. albicans pathogenesis have suggested several steps must occur before virulent infection, including early adhesion, invasion, and late tissue damage. However, the mechanism that triggers C. albicans transformation from yeast to hyphae form during infection has yet to be fully elucidated. This study used a systems biology approach to investigate C. albicans infection in zebrafish. The surviving fish were sampled at different post-infection time points to obtain time-lapsed, genome-wide transcriptomic data from both organisms, which were accompanied with in sync histological analyses. Principal component analysis (PCA) was used to analyze the dynamic gene expression profiles of significant variations in both C. albicans and zebrafish. The results categorized C. albicans infection into three progressing phases: adhesion, invasion, and damage. Such findings were highly supported by the corresponding histological analysis. Furthermore, the dynamic interspecies transcript profiling revealed that C. albicans activated its filamentous formation during invasion and the iron scavenging functions during the damage phases, whereas zebrafish ceased its iron homeostasis function following massive hemorrhage during the later stages of infection. Most of the immune related genes were expressed as the infection progressed from invasion to the damage phase. Such global, inter-species evidence of virulence-immune and iron competition dynamics during C. albicans infection could be crucial in understanding control fungal pathogenesis.

A genetic algorithm-based Boolean delay model of intracellular signal transduction in inflammation.

BACKGROUND: Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed. RESULTS: In present study, we employed a genetic algorithm-based Boolean model to represent NF-κB signaling pathway. We were able to capture feedback and crosstalk characteristics to enhance our understanding on the acute and chronic inflammatory response. Key network components affecting the response dynamics were identified. CONCLUSIONS: We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.

Computational modeling with forward and reverse engineering links signaling network and genomic regulatory responses: NF-kappaB signaling-induced gene expression responses in inflammation.

BACKGROUND: Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Diverse cellular stimuli create a wide variety of transcription factor activities through signal transduction pathways, resulting in different gene expression patterns. Understanding the relationship between external stimuli and the corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to identify the physiological consequences of environmental stimuli. RESULTS: We proposed a systematic approach that combines forward and reverse engineering to link the signal transduction cascade with the gene responses. To demonstrate the feasibility of our strategy, we focused on linking the NF-kappaB signaling pathway with the inflammatory gene regulatory responses because NF-kappaB has long been recognized to play a crucial role in inflammation. We first utilized forward engineering (Hybrid Functional Petri Nets) to construct the NF-kappaB signaling pathway and reverse engineering (Network Components Analysis) to build a gene regulatory network (GRN). Then, we demonstrated that the corresponding IKK profiles can be identified in the GRN and are consistent with the experimental validation of the IKK kinase assay. We found that the time-lapse gene expression of several cytokines and chemokines (TNF-alpha, IL-1, IL-6, CXCL1, CXCL2 and CCL3) is concordant with the NF-kappaB activity profile, and these genes have stronger influence strength within the GRN. Such regulatory effects have highlighted the crucial roles of NF-kappaB signaling in the acute inflammatory response and enhance our understanding of the systemic inflammatory response syndrome. CONCLUSION: We successfully identified and distinguished the corresponding signaling profiles among three microarray datasets with different stimuli strengths. In our model, the crucial genes of the NF-kappaB regulatory network were also identified to reflect the biological consequences of inflammation. With the experimental validation, our strategy is thus an effective solution to decipher cross-talk effects when attempting to integrate new kinetic parameters from other signal transduction pathways. The strategy also provides new insight for systems biology modeling to link any signal transduction pathways with the responses of downstream genes of interest.