Production of α-Bisabolol from metabolically engineered Escherichia coli

α-Bisabolol is a natural-occurring sesquiterpenoid with applications in cosmetics as whitening and soothing agent. It is synthesized from the universal precursors, isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), which are generated either through the mevalonate (MVA) pathway or the 2C-methyl-D-erythritol-4-phosphate (MEP) pathway. Farnesyl pyrophosphate (FPP) synthase (IspA) then catalyzes the condensation of IPP and DMAPP to the linear FPP, which is rearranged and cyclized to α-bisabolol by bisabolol synthases. Here, we compared the capacity of 5 α-bisabolol synthases from Lippia dulcis, Streptomyces citricolor, Santalum spicatum, Matricaria recutita, and Artemisia annua for α-bisabolol production. MVA pathway and FPP synthase were also overexpressed to supply sufficient FPP for bisabolol synthesis in the recombinant E. coli. Bisabolol synthase from M. recutita (MrBBS) shows the highest activity of bisabolol synthesis, and 75 mg/L/OD600 of bisabolol was produced in a test-tube culture. We further optimized the expression level of IspA and MrBBS by modulation their RBS strength. The 24 bisabolol synthesis operons with different RBSs were assessed for their performance on bisabolol synthesis. By this approach, the best strain is able to produce bisabolol with a capacity of 220mg/L/OD600 in a test tube culture. The consequence of host strain optimization led to an increase in bisabolol production to 300 mg/L/OD600, which presents a 4-fold increase over the initial engineered strain. This work was supported by a grant (NRF-2016R1A2B2010678) from the National Research Foundation, MSIP, Korea

Evaluation of Matusita Equation and Its Modified Expression for Determining Activation Energy Associated with Melt Crystallization

Both the Matusita equation and the modified Matusita equation for estimating the activation energy associated with non-isothermal crystallization were critically evaluated. The derivation for melts crystallization on cooling indicates that, unlike for the crystallization that occurs on heating, the term 1 - exp (-Delta G/RT) in the basic rate equation of crystal growth and the term depending on the initial temperature of the cooling process cannot be neglected. It is demonstrated that both the Matusita equation and its modified expression are only valid to estimate the activation energy associated with the crystallization that occurs on heating, but are inapplicable for the melt crystallization that occurs on cooling. It is suggested that the isoconversional methods of Friedman and Vyazovkin should be alternative to determine effective activation energy for melt crystallization that occurs on cooling.open1133sciescopu

Crystallization Kinetics and Mechanism of CaO-Al2O3-Based Mold Flux for Casting High-Aluminum TRIP Steels

Non-isothermal crystallization of the newly developed lime-alumina-based mold fluxes was investigated using differential scanning calorimetry. The crystallization kinetic parameters were determined by Ozawa equation, the combined Avrami-Ozawa equation, and the differential iso-conversional method of Friedman. It was found that Ozawa method failed to describe the non-isothermal crystallization behavior of the mold fluxes. The Avrami exponent determined by the combined Avrami-Ozawa equation indicates that the crystallization of cuspidine occurs through bulk nucleation and reaction-controlled three-dimensional growth, and then transforms to reaction-controlled two-dimensional growth at the crystallization later stage in lime-alumina-based mold fluxes with higher B2O3 content. For the mold fluxes with lower B2O3 content (10.8 mass pct), the crystallization of cuspidine is bulk nucleation and reaction-controlled two-dimensional growth at the crystallization primary stage followed by a diffusion-controlled two-dimensional growth process. The crystallization of CaF2 in mold flux originates from bulk nucleation and diffusion-controlled three-dimensional growth, which then transforms to two-dimensional growth. FE-SEM observations support these kinetic analysis results. The effective activation energy for cuspidine crystallization in the mold flux with higher B2O3 and Na2O contents increases as the crystallization progresses, and then decreases at the relative degree of crystallinity greater than 60 pct. The transition point of this trend approximately corresponds to the relative degree of crystallinity at which the crystallization mode of cuspidine transforms. For the mold fluxes with lower B2O3 and Na2O contents, the effective activation energy for cuspidine formation varies monotonically with the increase in the relative degree of crystallinity.open11149sciescopu

Boosting Learning for LDPC Codes to Improve the Error-Floor Performance

Low-density parity-check (LDPC) codes have been successfully commercialized in communication systems due to their strong error correction capabilities and simple decoding process. However, the error-floor phenomenon of LDPC codes, in which the error rate stops decreasing rapidly at a certain level, presents challenges for achieving extremely low error rates and deploying LDPC codes in scenarios demanding ultra-high reliability. In this work, we propose training methods for neural min-sum (NMS) decoders to eliminate the error-floor effect. First, by leveraging the boosting learning technique of ensemble networks, we divide the decoding network into two neural decoders and train the post decoder to be specialized for uncorrected words that the first decoder fails to correct. Secondly, to address the vanishing gradient issue in training, we introduce a block-wise training schedule that locally trains a block of weights while retraining the preceding block. Lastly, we show that assigning different weights to unsatisfied check nodes effectively lowers the error-floor with a minimal number of weights. By applying these training methods to standard LDPC codes, we achieve the best error-floor performance compared to other decoding methods. The proposed NMS decoder, optimized solely through novel training methods without additional modules, can be integrated into existing LDPC decoders without incurring extra hardware costs. The source code is available at https://github.com/ghy1228/LDPC_Error_Floor .Comment: 17 pages, 10 figure

How to Mask in Error Correction Code Transformer: Systematic and Double Masking

In communication and storage systems, error correction codes (ECCs) are pivotal in ensuring data reliability. As deep learning's applicability has broadened across diverse domains, there is a growing research focus on neural network-based decoders that outperform traditional decoding algorithms. Among these neural decoders, Error Correction Code Transformer (ECCT) has achieved the state-of-the-art performance, outperforming other methods by large margins. To further enhance the performance of ECCT, we propose two novel methods. First, leveraging the systematic encoding technique of ECCs, we introduce a new masking matrix for ECCT, aiming to improve the performance and reduce the computational complexity. Second, we propose a novel transformer architecture of ECCT called a double-masked ECCT. This architecture employs two different mask matrices in a parallel manner to learn more diverse features of the relationship between codeword bits in the masked self-attention blocks. Extensive simulation results show that the proposed double-masked ECCT outperforms the conventional ECCT, achieving the state-of-the-art decoding performance with significant margins.Comment: 8 pages, 5 figure

Performance-Based Multiobjective Optimal Seismic Retrofit Method for a Steel Moment-Resisting Frame Considering the Life-Cycle Cost

This study proposes a performance-based multiobjective optimization seismic retrofit method for steel moment-resisting frames. The brittle joints of pre-Northridge steel moment-resisting frames are retrofitted to achieve ductility; the method involves determining the position and number of connections to be retrofitted. The optimal solution is determined by applying the nondominated sorting genetic algorithm-II (NSGA-II), which acts as a multiobjective seismic retrofit optimization technique. As objective functions, the initial cost for the connection retrofit and lifetime seismic damage cost were selected, and a seismic performance level below the 5% interstory drift ratio was employed as a constraint condition. The proposed method was applied to the SAC benchmark three- and nine-story buildings, and several Pareto solutions were obtained. The optimized retrofit solutions indicated that the lifetime seismic damage cost decreased as the initial retrofit cost increased. Although every Pareto solution existed within a seismic performance boundary set by a constraint function, the seismic performance tended to increase with the initial retrofit cost. Analysis and economic assessment of the relations among the initial retrofit cost, lifetime seismic damage cost, total cost, and seismic performance of the derived Pareto solution allow building owners to make seismic retrofit decisions more rationally