Mercer Kernel Based Fuzzy Clustering Self-Adaptive Algorithm

A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

Beyond monogenetic rare variants:tackling the low rate of genetic diagnoses in predominantly antibody deficiency

Predominantly antibody deficiency(PAD)is the most prevalent form of primary immunodeficiency,and is characterized by broad clinical,immunological and genetic heterogeneity.Utilizing the current gold standard of whole exome sequencing for diagnosis,pathogenic gene variants are only identified in less than 20% of patients.While elucidation of the causal genes underlying PAD has provided many insights into the cellular and molecular mechanisms underpinning disease pathogenesis,many other genes may remain as yet undefined to enable definitive diagnosis,prognostic monitoring and targeted therapy of patients.Considering that many patients display a relatively late onset of disease presentation in their 2^(nd) or 3^(rd) decade of life,it is questionable whether a single genetic lesion underlies disease in all patients.Potentially,combined effects of other gene variants and/or non-genetic factors,including specific infections can drive disease presentation.In this review,we define(1)the clinical and immunological variability of PAD,(2)consider how genetic defects identified in PAD have given insight into B-cell immunobiology,(3)address recent technological advances in genomics and the challenges associated with identifying causal variants,and(4)discuss how functional validation of variants of unknown significance could potentially be translated into increased diagnostic rates,improved prognostic monitoring and personalized medicine for PAD patients.A multidisciplinary approach will be the key to curtailing the early mortality and high morbidity rates in this immune disorder.