Transformative research refers to research that shifts or disrupts established scientific paradigms. Notable examples include the discovery of high-temperature superconductivity that disrupted the theory established 30 years ago. Identifying potential transformative research early and accurately is important for funding agencies to maximize the impact of their investments. It also helps scientists identify and focus their attention on promising emerging works. This paper presents a data driven approach where citation patterns of scientific papers are analyzed to quantify how much a potential challenger idea shifts an established paradigm. The key idea is that transformative research creates an observable disruption in the structure of "information cascades," chains of references that can be traced back to the papers establishing some scientific paradigm. Such a disruption is visible soon after the challenger's introduction. We define a disruption score to quantify the disruption and develop an algorithm to compute it from a large citation network. Experimental results show that our approach can successfully identify transformative scientific papers that disrupt established paradigms in Physics and Computer Science, regardless of whether the challenger paradigm is an instant hit or a classic whose contribution is formally recognized with a Nobel Prize decades later.

Morphological dynamics of mitochondria is associated with key cellular processes related to aging and neuronal degenerative diseases, but the lack of standard quantification of mitochondrial morphology impedes systematic investigation. This paper presents an automated system for the quantification and classification of mitochondrial morphology. We discovered six morphological subtypes of mitochondria for objective quantification of mitochondrial morphology. These six subtypes are small globules, swollen globules, straight tubules, twisted tubules, branched tubules and loops. The subtyping was derived by applying consensus clustering to a huge collection of more than 200 thousand mitochondrial images extracted from 1422 micrographs of Chinese hamster ovary (CHO) cells treated with different drugs, and was validated by evidence of functional similarity reported in the literature. Quantitative statistics of subtype compositions in cells is useful for correlating drug response and mitochondrial dynamics. Combining the quantitative results with our biochemical studies about the effects of squamocin on CHO cells reveals new roles of Caspases in the regulatory mechanisms of mitochondrial dynamics. This system is not only of value to the mitochondrial field, but also applicable to the investigation of other subcellular organelle morphology.

MOTIVATION: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise. RESULTS: We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification. Calibration is essential for a practical quantification method because we need to measure the confidence of the quantification. We applied our method to quantify the degree of partial fragmentation of mitochondria in collections of fluorescent cell images. We show that with transformation, the quantification can be more accurate and sensitive than that without transformation. We also show that our method outperforms competing methods, including neighbourhood component analysis and the multi-variate drug profiling method by Loo et al. We illustrate its utility with a study of Annonaceous acetogenins, a family of compounds with drug potential. Our result reveals that squamocin induces more fragmented mitochondria than muricin A. AVAILABILITY: Mitochondrial cell images, their corresponding feature sets (SSLF and WSLF) and the source code of our proposed method are available at http://aiia.iis.sinica.edu.tw/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mobile IP (MIP) and mobile IP route optimization (MIP-RO) have been proposed to provide mobility management in the Internet. However, the MIP protocol suffers from triangular routing problem. MIP-RO adopts binding cache to avoid the triangular routing problem and provides smoother handoff. It is very difficult to enforce a binding cache upon each node in the entire Internet. Instead, we developed a more efficient handoff scheme, called mobile routing table (MRT) to enable the edge routers to take over the address binding. Nevertheless, we cannot demand all the edge routers to support MRT in the Internet. Therefore, this paper introduces five different schemes which relieve the limitation of the original MRT approach and cut down the triangular routing problem as much as possible. We analyze and compare both the standard MIP and the proposed approaches. The performance results are reported