Single molecule fluorescent in situ hybridization (smFISH) of C. elegans worms and embryos

In C. elegans, the expression pattern of a gene provides important clues to understanding its biological function. To accurately depict endogenous transcriptional activity, a highly sensitive method is required to measure transcript levels in the intact tissue across various developmental stages. Conventional RNA in situ hybridization methods using hapten- (biotin or digoxygenin) labeled RNA probes rely on antibody binding for visualization, and are thus only semi-quantitative at best (Raap et al. 1995; Levsky et al. 2003). Additionally, hapten-labeled probes are prone to diffuse localization (when conjugated with alkaline phosphatase), low sensitivity (when conjugated with fluorescent molecules), and non-specific probe binding. Here, we introduce a recently developed mRNA in situ hybridization method (Raj et al. 2008) that circumvents the above difficulties to give single molecule resolution of transcript detection

Learning from life-logging data by hybrid HMM: a case study on active states prediction

In this paper, we have proposed employing a hybrid classifier-hidden Markov model (HMM) as a supervised learning approach to recognize daily active states from sequential life-logging data collected from wearable sensors. We generate synthetic data from real dataset to cope with noise and incompleteness for training purpose and, in conjunction with HMM, propose using a multiobjective genetic programming (MOGP) classifier in comparison of the support vector machine (SVM) with variant kernels. We demonstrate that the system with either algorithm works effectively to recognize personal active states regarding medical reference. We also illustrate that MOGP yields generally better results than SVM without requiring an ad hoc kernel