ABSTRACT
The following study involved the utilization of dispersion polymerization to synthesize micron/nano-sized polystyrene (PS) spheres, which were then deposited onto a silicon substrate using the floating assembly method to form a long-range monolayer. Subsequently, dry etching techniques were utilized to create subwavelength structures. The adjustment of the stabilizer polyvinylpyrrolidone (PVP), together with changes in the monomer concentration, yielded PS spheres ranging from 500 nm to 5.6 µm in diameter. These PS spheres were suspended in a mixture of alcohol and deionized water before being arranged using the floating assembly method. The resulting tightly packed particle arrangement is attributed to van der Waals forces, Coulomb electrostatic forces between the PS spheres, and surface tension effects. The interplay of these forces was analyzed to comprehend the resulting structure. Dry etching, utilizing the PS spheres as masks, enabled the exploration of the effects of etching parameters on the resultant structures. Unlike traditional dry etching methods controlling RF power and etching gases, in the present study, we focused on adjusting the oxygen flow rate to achieve cylindrical, conical, and parabolic etched structures.
ABSTRACT
BACKGROUND: Identification of epistatic interactions provides a systematic way for exploring associations among different single nucleotide polymorphism (SNP) and complex diseases. Although considerable progress has been made in epistasis detection, efficiently and accurately identifying epistatic interactions remains a challenge due to the intensive growth of measuring SNP combinations. RESULTS: In this work, we formulate the detection of epistatic interactions by a combinational optimization problem, and propose a novel evolutionary-based framework, called GEP-EpiSeeker, to detect epistatic interactions using Gene Expression Programming. In GEP-EpiSeeker, we propose several tailor-made chromosome rules to describe SNP combinations, and incorporate Bayesian network-based fitness evaluation into the evolution of tailor-made chromosomes to find suspected SNP combinations, and adopt the Chi-square test to identify optimal solutions from suspected SNP combinations. Moreover, to improve the convergence and accuracy of the algorithm, we design two genetic operators with multiple and adjacent mutations and an adaptive genetic manipulation method with fuzzy control to efficiently manipulate the evolution of tailor-made chromosomes. We compared GEP-EpiSeeker with state-of-the-art methods including BEAM, BOOST, AntEpiSeeker, MACOED, and EACO in terms of power, recall, precision and F1-score on the GWAS datasets of 12 DME disease models and 10 DNME disease models. Our experimental results show that GEP-EpiSeeker outperforms comparative methods. CONCLUSIONS: Here we presented a novel method named GEP-EpiSeeker, based on the Gene Expression Programming algorithm, to identify epistatic interactions in Genome-wide Association Studies. The results indicate that GEP-EpiSeeker could be a promising alternative to the existing methods in epistasis detection and will provide a new way for accurately identifying epistasis.
