Systematic Optimization Strategy Research Articles

Enhanced production of biotin in Pseudomonas mutabilis with systematic culture optimization and fed-batch strategies

Enhanced production of biotin in Pseudomonas mutabilis with systematic culture optimization and fed-batch strategies

Systematic optimization of UCNPs-LFA for Helicobacter pylori nucleic acid detection at point-of-care.

Helicobacter pylori (Hp) prevail globally as the primary cause of gastritis, gastric ulcer, and potential gastric cancer, highlighting the need for rapid and precise point-of-care (POC) detection of Hp nucleic acid. Upconversion nanoparticle-based lateral flow assay (UCNPs-LFA) exhibit great potential in POC detection, due to their high optical stability and absence of background fluorescence. However, insufficient sensitivity for nucleic acid detection remains a key challenge. This study systematically optimizes UCNPs-LFA by focusing on target capture, signal transduction, signal separation, and signal analysis, to enhance its detection capabilities for Hp nucleic acid. The optimized UCNPs-LFA platform features a significantly decreased detection limit, a broadened detection range, and high reliability. Results demonstrate that the limit of detection (LOD) is 25 fM, a 105-fold improvement over the initial platform. This systematic optimization strategy is versatile and can be applied to optimize other nanoparticle-based LFAs.

Novel heterocyclic amide derivatives containing a diphenylmethyl moiety: systematic optimizations, synthesis, antifungal activity and action mechanism.

The development of fungicides with low cross resistance, high efficacy and low resistance plays a central role in protecting crops, reducing yield losses, improving quality and maintaining global food security. Based on this important role, after a systematic optimization strategy, novel heterocyclic amide derivatives bearing diphenylmethyl fragment were screened, synthesized and verified with the spectrographic and x-ray diffraction analysis. In this study, the aforementioned optimization obtained compound B19 that was measured for antifungal activity against Rhizoctonia solani (median effective concentration, EC50 = 1.11 μg mL-1). Meanwhile, the anti-R. solani protective effect (79.34%) of compound B19 was evaluated in vivo at 100 μg mL-1, which is comparable to that of the control agent fluxapyroxad (80.67%). Thence, morphological observations revealed that compound B19 induced mycelium disruption and shrinking, mitochondrial number reduction and apoptosis acceleration, consistent with the results of the mitochondrial membrane potential and cell membrane permeability. Further investigations found that the potential target enzyme of compound B19 was SDH, which exerted fluorescence quenching dynamic curves similar to that of the commercialized SDHI fluxapyroxad. Additionally, research by molecular docking and MD simulations demonstrated that compound B19 had a similar binding mode acting on the surrounding residues in the SDH active pocket to that offluxapyroxad. The above results demonstrated that heterocyclic amide derivatives containing a diphenylmethyl moiety are promising scaffolds for targeting SDH of fungi and provide valuable antifungal leads with the potential to develop new SDH inhibitors. © 2024 Society of Chemical Industry.

Optimizing heterostructure parameters towards enhanced toughening in micro/nano-reinforced bimodal-grained Al alloy composites

We manipulated soft coarse-grained (CG) domains to design optimized and toughened B4Cp/6061Al composites, featuring bimodal domains with in-situ MgO nanoparticles (n-MgO) and ex-situ carbon nanotubes (CNTs). Manipulating CG fractions influenced heterostructure parameters such as CG band width and domain distribution. A systematic optimization strategy integrating intrinsic/extrinsic toughening and strengthening mechanisms identified optimal conditions for maximizing strength-ductility-toughness. The optimal 20:80 CG-to-ultrafine-grain (UFG) weight ratio offered an ultimate strength of 550 MPa and ∼7 % elongation. Intrinsic toughening mechanisms enhanced UFG domain dislocation storage capacity. Multiscale analysis of crack behavior revealed pronounced crack-blunting in well-dispersed CG domains. Extrinsic toughening mechanisms included nanobridge formation, crack-deflection, micro-crack proliferation, and crack-branching. Micromechanical behavior was examined using the strain gradient model. Designing strong and ductile micro/nano-reinforced bimodal grained composites requires selecting a smaller amount of CG domains, a CG band width larger than double the interface affected zone (IAZ), and larger grain sizes in the CG domains.

Quality Optimizing Teaching Decisions in Flipped Classrooms Through Data-Driven Strategies

In the context of digitalized educational research, the rapid advancement of internet technologies and big data has catalyzed the transformation of traditional teaching models. The flipped classroom, recognized for its flexibility and efficiency, has garnered significant attention. However, the challenge of scientifically optimizing teaching decisions in flipped classrooms to maximize educational outcomes remains critical. Previous studies have achieved some progress in optimizing teaching decisions within flipped classrooms, yet they often suffer from a lack of methodological diversity, inadequate consideration of multi-level constraints, and struggle to adapt to dynamic teaching environments. Addressing these deficiencies, this research introduces a hybrid evolutionary algorithm combining differential evolution and greedy backtracking. Defined and classified constraints within flipped classroom teaching decisions, the construction of constraint networks, and the creation of multi-level decision spaces are transformed and solved through this hybrid algorithm, offering a systematic optimization strategy. Case analysis confirms the effectiveness and practicality of the proposed method, aiming to bolster decision-making in flipped classrooms and advance the development of digitalized teaching.

The sensitive detection of low molecular mass peptide drugs in dried blood spots by solid-phase extraction and LC-HRMS.

Dried blood spot (DBS) technique has become a new popular topic in anti-doping field in recent years due to its advantages of sample stability and easy operation. It can be employed as a supplementary method to routine urine analysis. However, the small volume of DBS samples (usually 10-20 μL) significantly reduces the application value of this technique. Therefore, the development of sensitive detection methods for the analysis of prohibited substances in DBS is particularly important. In this study, based on the characteristics of low molecular mass peptide (LMMP) drugs, systematic optimization strategies were utilized for the first time to establish a sensitive detection method for LMMPs in DBS. Without using DMSO to enhance mass spectrometry ionization efficiency of peptides, the limits of detection (LOD) ranged between 0.05 and 3.74ng/mL, significantly better than the previously reported method (0.5-20ng/mL). This method was validated according to the guidelines of the World Anti-Doping Agency (WADA), and corresponding post-administration study was conducted, demonstrating that the method could be applied to routine analysis of LMMP drugs in DBS. Moreover, since DMSO is not involved, this method also has the potential to simultaneously detect both LMMP and small molecular drugs.

Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning

Achieving highly accurate predictions based on less data with multiple formulations has become a significant challenge. Unlike the traditional prediction model that ignores the similarities and differences between multi-formula batteries, the proposed multi-source transfer RUL prediction is an effective way to jointly transfer several samples and merge degradation information from multi-source domains, thereby taking full advantage of the information. Aiming at the problem of negative transfer and insufficient transfer when multi-source domains are available, dynamic time warping is employed to adaptively select transferable samples, ignoring the local individual differences. The least absolute shrinkage and selection operator are utilized to generate an optimal subset and allocate personalized weights. Then, an optimization strategy for the cycle life tests is proposed by automatically giving a reasonable threshold of stopping the test. The proposed method is verified based on an actual dataset with multiple formulations collected under different test conditions from a battery manufacturer. Experimental results show that the proposed method has high accuracy based on even a small amount of data. The systematic optimization strategy can significantly reduce the time and cost of the test.

Optimization of the crashworthiness design of carbon fiber-reinforced polymer bumper beam

To improve the safety performance and light weight of the bumper subsystem under low-speed collisions with multiple loads, this study develops a systematic optimization strategy. First, the accuracy of the finite element model is verified through experiments. Meanwhile, the crashworthiness of the three different bumper beams made of aluminum alloy, high-strength steel, and carbon fiber-reinforced polymer (CFRP) is compared and analyzed. Second, the optimal combination of single-layer thickness and lay-up angle of the CFRP bumper beam is discussed. Finally, an optimization strategy combining Hammersley experimental design, hybrid approximation model, NSGA-II algorithm, combined weights, and technique for order preference by similarity to an ideal solution and grey relational analysis (TOPSIS&GRA) integrated decision is proposed and applied to the optimal design of the CFRP bumper beam. The optimization results show that the optimized CFRP bumper beam is 57.18% lighter than the original aluminum composite bumper beam while meeting the requirements of crashworthiness.

A critical review on composite solid electrolytes for lithium batteries: Design strategies and interface engineering

The rapid development of new energy vehicles and 5G communication technologies has led to higher demands for the safety, energy density, and cycle performance of lithium-ion batteries as power sources. However, the currently used liquid carbonate compounds in commercial lithium-ion battery electrolytes pose potential safety hazards such as leakage, swelling, corrosion, and flammability. Solid electrolytes can be used to mitigate these risks and create a safer lithium battery. Furthermore, high-energy density can be achieved by using solid electrolytes along with high-voltage cathode and metal lithium anode. Two types of solid electrolytes are generally used: inorganic solid electrolytes and polymer solid electrolytes. Inorganic solid electrolytes have high ionic conductivity, electrochemical stability window, and mechanical strength, but suffer from large solid/solid contact resistance between the electrode and electrolyte. Polymer solid electrolytes have good flexibility, processability, and contact interface properties, but low room temperature ionic conductivity, necessitating operation at elevated temperatures. Composite solid electrolytes (CSEs) are a promising alternative because they offer light weight and flexibility, like polymers, as well as the strength and stability of inorganic electrolytes. This paper presents a comprehensive review of recent advances in CSEs to help researchers optimize CSE composition and interactions for practical applications. It covers the development history of solid-state electrolytes, CSE properties with respect to nanofillers, morphology, and polymer types, and also discusses the lithium-ion transport mechanism of the composite electrolyte, and the methods of engineering interfaces with the positive and negative electrodes. Overall, the paper aims to provide an outlook on the potential applications of CSEs in solid-state lithium batteries, and to inspire further research aimed at the development of more systematic optimization strategies for CSEs.

Metabolic Engineering of Saccharomyces cerevisiae for Vitamin B5 Production

Vitamin B5, also called d-pantothenic acid, is an essential vitamin in the human body and is widely used in pharmaceuticals, nutritional supplements, food, and cosmetics. However, few studies have investigated the microbial production of d-pantothenic acid, especially in Saccharomyces cerevisiae. By employing a systematic optimization strategy, we screened seven key genes in d-pantothenic acid biosynthesis from diverse species, including bacteria, yeast, fungi, algae, plants, animals, etc., and constructed an efficient heterologous d-pantothenic acid pathway in S. cerevisiae. By adjusting the copy number of the pathway modules, knocking out the endogenous bypass gene, balancing NADPH utilization, and regulating the GAL inducible system, a high-yield d-pantothenic acid-producing strain, DPA171, which can regulate gene expression using glucose, was constructed. By optimizing fed-batch fermentation, DPA171 produced 4.1 g/L d-pantothenic acid, which is the highest titer in S. cerevisiae to date. This study provides guidance for the development of vitamin B5 microbial cell factories.

Embroidery Electro-Textile Patch Antenna Modeling and Optimization Strategies With Improved Accuracy and Efficiency

In this article, we present systematic modeling and optimization strategies for embroidery electrotextile patch antennas, including material characterization, detailed modeling, stitch density study, and surrogate-based optimization. A detailed full-wave model that accounts for the complex embroidery features is proposed, with the effects of stitch density analyzed and validated through measurements for both rectangular and E-shaped patch antennas. A textile tailored surrogate optimization technique is then implemented to accelerate the computationally expensive full-wave evaluation for the detailed full-wave model, saving almost an order of magnitude of the computation time compared with canonical optimization. The methodologies presented in this article provide a pathway for both improved accuracy and efficiency in designing embroidery electrotextile patch antennas.

Multi-Objective RANS Aerodynamic Optimization of a Hypersonic Intake Ramp at Mach 5

The work describes a systematic optimization strategy for designing hypersonic inlet intakes. A Reynolds-averaged Navier-Stokes database is mined using genetic algorithms to develop ideal designs for a priori defined targets. An intake geometry from the literature is adopted as a baseline. Thus, a steady-state numerical assessment is validated and the computational grid is tuned under nominal operating conditions. Following validation tasks, the model is used for multi-objective optimization. The latter aims at minimizing the drag coefficient while boosting the static and total pressure ratios, respectively. The Pareto optimal solutions are analyzed, emphasizing the flow patterns that result in the improvements. Although the approach is applied to a specific setup, the method is entirely general, offering a valuable flowchart for designing super/hypersonic inlets. Notably, because high-quality computational fluid dynamics strategies drive the innovation process, the latter accounts for the complex dynamics of such devices from the early design stages, including shock-wave/boundary-layer interactions and recirculating flow portions in the geometrical shaping.

A general mixed-integer programming framework for efficient modeling, integration and optimization of thermodynamic cycle-based energy systems

Thermodynamic cycles are imperative systems for energy conversion, and optimization has been an important tool to improve their thermo-economic benefits. However, off-the-shelf software is often limited by long computational time, inflexibility for modeling and numerical difficulties. Here, we present an equation-based optimization framework featuring: (1) an inclusive modeling structure applicable to various thermodynamic cycles design problems; (2) versatile thermodynamic properties estimation for different working fluids; (3) capability of working fluids optimization with complex composition; (4) an efficient formulation method to model potential phase change of streams in simple/complicated heat exchangers; (5) a systematic optimization strategy to improve solution efficiency. The framework is formulated as mixed-integer nonlinear programming models, and its performance is demonstrated by two thermodynamic cycle optimization problems and one system design problem, including a supercritical CO2 Brayton cycle, cryogenic cycle (PRICO), and concentrated solar power (CSP) system. The Brayton cycle case serves as an illustrative example to show thermodynamic cycle design combining the consideration of stream properties calculation, unit operations, and flowsheet optimization. The PRICO example shows the efficacy of the proposed framework in simultaneously optimizing flowsheet and working fluids composition with complex heat exchangers and stream phase unknown a priori. The CSP case demonstrates that the framework can be extended to system optimization problems with high accuracy multi-parameter equation of state. Results prove that the framework is efficient and effective in solving thermodynamic cycle and related energy systems optimization problems.

Design criteria to fabricate plasmonic gold nanomaterials for surface-enhanced Raman scattering (SERS)-based biosensing

The discovery of noble metal plasmonic nanoparticles (PNPs) has introduced surface-enhanced Raman scattering (SERS) as a highly sensitive and specific bioanalytical technique with greater potential in point-of-need disease diagnosis. This Tutorial provides an overview of the principles governing a gold PNP-based biosensor design for sensitive and reliable SERS-based detection of disease biomarkers. First, we will highlight the optical transducer properties of PNPs, the principles of SERS, the benefits of SERS detection, and the modes of SERS for biomarker detection. The analytical performance (sensitivity and specificity) and the reliability (accuracy and reproducibility) of a SERS biosensor are mainly dictated by (i) the chemical and optical transducer properties of PNPs, (ii) the functional nano interface, where the interaction(s) between PNPs and target biomolecules take place, and (iii) SERS data acquisition and evaluation metrics. Maintaining a balance between SERS signal enhancement and reproducibility is critical for advancing the field deployment of SERS technologies. However, the reproducibility of SERS biosensors is often overlooked in lieu of the assay sensitivity. Consequently, next, we will discuss the systematic optimization strategies for fabricating gold PNPs as SERS substrates and designing their functional interface to design SERS biosensors with sufficient sensitivity, specificity, and reproducibility. We will highlight the choice of PNPs and their integration into biosensing platforms depending on the mode of SERS detection. Last, we will discuss the SERS data acquisition and performance evaluation as an integral part of the SERS biosensors development workflow.

Harnessing a Novel Machine-Learning-Assisted Evolutionary Algorithm to Co-optimize Three Characteristics of an Electrospun Oil Sorbent.

The optimization of materials is challenging as it often involves simultaneous manipulation of an assembly of condition parameters, which generates an enormous combinational space. Thus, optimization models and algorithms are widely adopted to accelerate material design and optimization. However, most optimization strategies can poorly handle multiple parameters simultaneously with limited prior knowledge. Herein, we describe a novel systematic optimization strategy, namely, machine-learning-assisted differential evolution, which combines machine learning and the evolutionary algorithm together, for zero-prior-data, rapid, and simultaneous optimization of multiple objectives. The strategy enables the evolutionary algorithm to "learn" so as to accelerate the optimization process, and also to identify quantitative interactions between the condition parameters and functional characteristics of the material. The performance of the strategy is verified by in silico simulations, as well as an application on simultaneously optimizing three characteristics, namely, water contact angle, oil absorption capacity, and mechanical strength, of an electrospun polystyrene/polyacrylonitrile (PS/PAN) material as a potential sorbent for a marine oil spill. With only 50 tests, the optimal fabrication parameters were successfully located from a combinatorial space of 50 000 possibilities. The presented platform technique offers a universal enabling technology to identify the optimal conditions rapidly from a daunting parameter space to synthesize materials with multiple desired functionalities.

Systematic Metabolic Engineering of Saccharomyces cerevisiae for Lycopene Overproduction.

Lycopene is widely used in foods, cosmetics, nutritional supplements, and pharmaceuticals. Microbial production of lycopene has been intensively studied. However, there are few systematic engineering studies on Saccharomyces cerevisiae aimed at achieving high-yield lycopene production. In the current study, by employing a systematic optimization strategy, we screened the key lycopene biosynthetic genes, crtE, crtB, and crtI, from diverse organisms. By adjusting the copy number of these three key genes, knocking out endogenous bypass genes, increasing the supply of the precursor acetyl-CoA, balancing NADPH utilization, and regulating the GAL-inducible system, we constructed a high-yield lycopene-producing strain BS106, which can produce 310 mg/L lycopene in shake-flask fermentation, with gene expression controlled by glucose. In optimized two-stage fed-batch fermentation, BS106 produced 3.28 g/L lycopene in a 7 L fermenter, which is the highest concentration achieved in S. cerevisiae to date. It will decrease the consumption of tomatoes for lycopene extraction and increase the market supply of lycopene.

Systematic multi-level optimization design and dynamic control of less-rare-earth hybrid permanent magnet motor for all-climatic electric vehicles

Motors that convert electrical energy to mechanical energy, have been considered as one of the most crucial components of all-climatic electric vehicles (EVs). In order to realize high-quality energy conversion, motor optimization has attracted considerable attention in the research field of motors. In this paper, systematic multi-level optimization design and dynamic control strategy is proposed for a less-rare-earth hybrid permanent magnet (LRE-HPM) motor. In the proposed systematic optimization strategy, the design requirements of the motor design level and control level are considered comprehensively. In the motor design level, the comprehensive sensitivity analysis is adopted to stratify design parameters, and the response surface method and multi-objective genetic algorithm are implemented respectively. In the motor control level, the resonance compensation strategy is established to further suppress torque ripple and speed vibration. Finally, a prototype motor is built and tested. Both simulation and experimental results not only verify the effectiveness of the proposed systematic optimization and dynamic control strategy, but also offer a research orientation for realizing high-quality energy conversion of all-climatic EVs.

Systematic Optimization of Limonene Production in Engineered Escherichia coli.

Limonene, a cyclic monoterpene, is widely used in food and cosmetics industries as well as in agriculture. In the work described herein, employing a systematic optimization strategy, we constructed an efficient platform for producing limonene via the heterologous mevalonate pathway in Escherichia coli. By site-directed mutation of EfMvaS and tuning the initial translation of EfMvaE and EfMvaSA110G through ribosome binding site engineering, the upstream module for overproducing mevalonate was obtained. Expression of MmMK with ScPMK, ScPMD, and ScIDI under FAB80 promoter resulted in an efficient midstream module to produce 181.73 mg/L of limonene. Subsequently, coexpression of SlNPPS and MsLS in the downstream module led to a great improvement of limonene production to 694.61 mg/L. Finally, metabolically engineered strain ELIM78 produced 1.29 g/L of limonene in 84 h by fed-batch fermentation in a shake-flask. This is the first report on limonene biosynthesis in E. coli using neryl pyrophosphate synthase, which has promising potential for producing other monoterpenes.

Optimization of microflow LC-MS/MS and its utility in quantitative discovery bioanalysis.

Aim: The sensitivity advantage of microflow LC (μFLC)-MS/MS is potentially impactful for challenging compounds not detectable by conventional flow LC-MS/MS in drug discovery bioanalysis. Relatively new to μFLC technology, discovery bioanalytical scientists would benefit from an effective strategy for method development and optimization. Results: A systematic μFLC-MS/MS method optimization approach was developed in this study. Withoptimized conditions, μFLC-MS/MS demonstrated an improved sensitivity compared with conventional LC-MS/MS analysis, ranging from 6× to 49× (by peak area) depending on the compounds, with acceptable analytical performance and robustness. The optimized conditions demonstrated universal applicability to various compounds of diverse properties. Conclusion: The systematic method optimization strategy, and the general applicability of the optimized conditions could facilitate the routine utilization of μFLC in quantitative discovery bioanalysis.

Tailoring a spectral line detection system for applications in dissolved gas analysis

In the process of developing a simple and highly sensitive dissolved gas analysis (DGA) sensor system, applicable for surveillance of the operating conditions of high-voltage equipment, a systematic optimization strategy has been conducted to allow detecting low concentration levels of DGA target molecules, by use of the harmonic detection technique, without use of long interaction length, multipass cells. Gas phase experiments were carried out by use of carbon monoxide — one of the target molecules in DGA— as a model to evaluate the limitations in detection sensitivity imposed by electronic noise, etalon effects, optical interference noise and mode hopping in the system’s optical assembly. By properly setting up the electronic detection system and designing the optical circuit and measurement cell, it was possible to detect a minimum carbon monoxide concentration of 90 ppm with a single pass, 25.4 cm cell, around the 1568 nm absorption line of the gas. This detection sensitivity, together with the simplicity of the measurement principle, allows establishing the proposed configuration as a feasible alternative for practical use in real-time, DGA of insulating oils of high-voltage transformers.