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Androgenetic alopecia (AGA) is a non-scarring and progressive form of hair loss occurring in both men and women. Although genetic predisposition and sex steroid hormones are the main causes, many factors remain unknown, and various extrinsic factors can negatively affect the lifespan of hair. We investigated skin–gut axis microorganisms as potential exogenous factors causing AGA, through comparative analyses of the scalp and gut microbiome in individuals with and without AGA in a Korean cohort. Using 16S rRNA gene sequencing, we characterized the scalp and gut microbiomes of 141 individuals divided into groups by sex and presence of AGA. Alpha diversity indices in the scalp microbiome were generally higher in individuals with AGA than in healthy controls. These indices showed a strong negative correlation with scalp-inhabitant bacteria (Cutibacterium and Staphylococcus), indicating that the appearance of non-inhabitant bacteria increases as hair loss progresses. No significant differences in diversity were observed between the gut microbiomes. However, bacterial functional differences, such as bile acid synthesis and bacterial invasion of epithelial cells, which are related to intestinal homeostasis, were observed. The networks of the scalp and gut microbiome were more complex and denser with higher values of the network topology statistic coefficient values (i.e., transitivity, density, and degree centrality) and more unique associations in individuals with AGA than in healthy controls. Our findings reveal a link between skin–gut microorganisms and AGA, indicating the former’s potential involvement in the latter’s development. Additionally, these results provide evidence for the development of cosmetics and therapeutics using microorganisms and metabolites involved in AGA.

1. Propagation modes in optical microfiber, 2. Estimation of minimum beat length, 3. Theoretical analysis of modal interference, 4. Efficiency of laser pumping, 5. Fabrication of tapered microfiber, 6. Laser characteristics, 7. Spectral filtering of

A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.