Exploring the relationship between fractal features and bacterial essential genes

Essential genes are indispensable for the survival of an organism in optimal conditions.Rapid and accurate identifications of new essential genes are of great theoretical and practical significance.Exploring features with predictive power is fundamental for this.Here,we calculate six fractal features from primary gene and protein sequences and then explore their relationship with gene essentiality by statistical analysis and machine learning-based methods.The models are applied to all the currently available identified genes in 27 bacteria from the database of essential genes（DEG）.It is found that the fractal features of essential genes generally differ from those of non-essential genes.The fractal features are used to ascertain the parameters of two machine learning classifiers：Na¨？ve Bayes and Random Forest.The area under the curve（AUC） of both classifiers show that each fractal feature is satisfactorily discriminative between essential genes and non-essential genes individually.And,although significant correlations exist among fractal features,gene essentiality can also be reliably predicted by various combinations of them.Thus,the fractal features analyzed in our study can be used not only to construct a good essentiality classifier alone,but also to be significant contributors for computational tools identifying essential genes.

Assessment of glaucoma using extreme learning machine and fractal feature analysis

Dear Sir,Iam Dr.Kavitha S,from the Department of Electronics and Communication Engineering,Nandha Engineering College,Erode,Tamil Nadu,India.I write to present the detection of glaucoma using extreme learning machine(ELM)and fractal feature analysis.Glaucoma is the second most frequent cause of permanent blindness in industrial

Fractal Characteristic of Soil in Typical Debris Flow-Triggering Region:A Case Study in Jiangjia Ravine of Dongchuan, Yunnan

The structural features of soil in debris flow-triggering region play an important role in the formation and evolution of debris flow. In this paper, a case study on the fractal of soil particle-size distribution （PSDFs） and pore-solid （PSFs） in Jiangjia Ravine was conducted. The results revealed that the soil in Jiangjia Ravine had significant fractal features and its PSDF and PSF had the same variation trend despite different type of soils in debris flow-triggering region： residual soil （RS） 〉 debris flow deposit （DFD）~clinosol （CL）, their fractal dimension of PSDFs are respectively between 2.62 and 2.96, 2.52 and 2.68, 2.37 and 2.52; and the fractal dimension of PSFs are respectively between 2. 75 and 2.95, 2. 57 and 2. 72, 2.59 and 2.64. The fractal dimension of soil reflected its complexity as a self-organizing system and was closely related to the evolution of soil in debris flow- triggering region.

Pulse-to-pulse periodic signal sorting features and feature extraction in radar emitter pulse sequences

A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting.

Insights into Pore Structure and Fractal Characteristics of the Lower Cambrian Niutitang Formation Shale on the Yangtze Platform,South China

Shales from the Lower Cambrian Niutitang Formation of Yangtze Platform have been widely investigated due to its shale gas potential.To better illustrate the pore structure and fractal characteristics of shale,a series of experiments were conducted on outcrop samples from the Lower Cambrian Niutitang Formation on Yangtze Platform,including X-ray diffraction(XRD),field-emission scanning electron microscopy(FE-SEM) and low-temperature nitrogen adsorption.Frenkel-Halsey-Hill(FHH) model was adopted to calculate the fractal dimensions.Furthermore,the relationships between fractal dimensions and pore structure parameters and mineral composition are discussed.FE-SEM observation results show that interparticle pores are most developed in shale,followed by intraparticle pores.This study identified the fractal dimensions D1(ranging from 2.558 0 to 2.710 2) and D2(ranging from 2.541 5 to 2.765 2).The pore structure of the Niutitang Formation shale is primarily controlled by quartz and clay content.Fractal dimensions are able to characterize the pore structure complexity of Niutitang Formation shale because D1 and D2 correlate well with average pore diameter and quartz content.