If truly thin embedded and human worn flexible electronics are to become a commercial reality for wearable electronics, medical devices, and internet of things tags, effective energy storage technologies that safely and robustly match the mechanical flexibility of the overall system form factor are required. At the same time, the energy and transient power needs of functions such as wireless connectivity, information display, and high sample rate sensing must be supported. These capabilities have time-dependent power and current requirements often not captured in simple energy and capacity metrics. In this paper, a progression of energy storage approaches, challenges and learning experiences will be presented from the perspective of an energy storage technology developer. The essential requirements for energy storage for feature-driven applications in flexible electronics are addressed with the goal of finding the most compelling fit between products needs, consumer safety and the technology capabilities of different energy storage approaches. Micropower modules from supercapacitors to microbatteries and their limitations for flexible electronics will be discussed in terms of capacity, power and charge retention as the starting point. Following this discussion, limitations of lithium technologies in this flexible and thin (<; 1 mm) application space are also outlined. This paper then presents a review of key requirements for energy storage for high functionality flexible electronics prototype systems and some approaches that have been explored to meet those needs. This leads to the conclusion that safe, low cost, flexible electronics energy storage requirements may be most appropriately met using intrinsically stable battery chemistry. Furthermore, such a materials approach allows for simpler lower cost processing and packaging, such as additive printing and roll to roll processing of thin and therefore more mechanically flexible cells. Examples and performance data from such a zinc polymer battery technology are given and compared to other thin and flexible battery approaches.

MOTIVATION: Tumors acquire many chromosomal amplifications, and those acquired early in the lifespan of the tumor may be not only important for tumor growth but also can be used for diagnostic purposes. Many methods infer the order of the accumulation of abnormalities based on their occurrence in a large cohort of patients. Recently, Durinck et al. (2011) and Greenman et al. (2012) developed methods to order a single tumor's chromosomal amplifications based on the patterns of mutations accumulated within those regions. This method offers an unprecedented opportunity to assess the etiology of a single tumor sample, but has not been widely evaluated. RESULTS: We show that the model for timing chromosomal amplifications is limited in scope, particularly for regions with high levels of amplification. We also show that the estimation of the order of events can be sensitive for events that occur early in the progression of the tumor and that the partial maximum likelihood method of Greenman et al. (2012) can give biased estimates, particularly for moderate read coverage or normal contamination. We propose a maximum-likelihood estimation procedure that fully accounts for sequencing variability and show that it outperforms the partial maximum-likelihood estimation method. We also propose a Bayesian estimation procedure that stabilizes the estimates in certain settings. We implement these methods on a small number of ovarian tumors, and the results suggest possible differences in how the tumors acquired amplifications. AVAILABILITY AND IMPLEMENTATION: We provide implementation of these methods in an R package cancerTiming, which is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.R-project.org/.