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Exponentially Weighted Moving Average (EWMA) plans for negative binomial counts with a non-homogeneous (time-varying) mean are developed for monitoring disease counts. These plans are used to identify unusual disease outbreaks or unusual epidemics. Time-varying means are typical for disease counts. The recommended surveillance plan in this article differs from the traditional approach of using standardized forecast errors based on the normality assumption, which suffers assumption concerns. The article demonstrates that the proposed EWMA plan has efficient detection properties for signaling unusually large outbreaks. These plans may be a useful tool for epidemiologists. Keywords: Control charts, EWMA, monitoring, negative binomial counts, 1. Introduction Compulsory reporting of several diseases to health authorities is used to facilitate public health monitoring and disease control. Epidemiologists monitor counts of these reportable diseases and decide when [...]

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.

Rice (Oryza sativa L. cv. IR64) was grown in split-root systems to analyze long-distance drought signaling within root systems. This in turn underpins how root systems in heterogeneous soils adapt to drought. The approach was to compare four root tissues: (1) fully watered; (2) fully droughted and split-root systems where (3) one-half was watered and (4) the other half was droughted. This was specifically aimed at identifying how droughted root tissues altered the proteome of adjacent wet roots by hormone signals and how wet roots reciprocally affected dry roots hydraulically. Quantitative label-free shotgun proteomic analysis of four different root tissues resulted in identification of 1487 nonredundant proteins, with nearly 900 proteins present in triplicate in each treatment. Drought caused surprising changes in expression, most notably in partially droughted roots where 38% of proteins were altered in level compared to adjacent watered roots. Specific functional groups changed consistently in drought. Pathogenesis-related proteins were generally up-regulated in response to drought and heat-shock proteins were totally absent in roots of fully watered plants. Proteins involved in transport and oxidation-reduction reactions were also highly dependent upon drought signals, with the former largely absent in roots receiving a drought signal while oxidation-reduction proteins were strongly present during drought. Finally, two functionally contrasting protein families were compared to validate our approach, showing that nine tubulins were strongly reduced in droughted roots while six chitinases were up-regulated, even when the signal arrived remotely from adjacent droughted roots.