Effects of chloride on paramagnetic coupling of manganese in calcium chloride-washed photosystem II preparations

The effect of chloride on paramagnetic coupling of manganese in the oxygen-evolving complex of CaCl2--washed PS II preparations was examined using Q-band ESR. When these PS II preparations were depleted of chloride, a strong 6-line ESR signal characteristic of protein-bound, uncoupled manganese was observed. Incubation at high chloride concentrations caused the disappearance of this signal. By repeated removal and addition of chloride, the signal could be cycled on and off without loss of bound manganese. When in a chloride-depleted state, the ESR-detectable protein-bound manganese could be removed by treatment with EDTA. Subsequent heating of EDTA-treated preparations revealed a second pool of protein-bound manganese associated with PS II. One of these pools requires a high concentration of chloride to maintain paramagnetic coupling while the second pool (within the limits of our observations) does not appear to require chloride for the maintenance of the paramagnetically coupled state. © 1986

Predictions Generated from a Simulation Engine for Gene Expression Micro-arrays for use in Research Laboratories

In this paper we introduce the technical components, the biology and data science involved in the use of microarray technology in biological and clinical research. We discuss how laborious experimental protocols involved in obtaining this data used in laboratories could benefit from using simulations of the data. We discuss the approach used in the simulation engine from [7]. We use this simulation engine to generate a prediction tool in Power BI, a Microsoft, business intelligence tool for analytics and data visualization [22]. This tool could be used in any laboratory using micro-arrays to improve experimental design by comparing how predicted signal intensity compares to observed signal intensity. Signal intensity in micro-arrays is a proxy for level of gene expression in cells. We suggest further development avenues for the prediction tool

Predictions Generated from a Simulation Engine for Gene Expression Micro-arrays for use in Research Laboratories

In this paper we introduce the technical components, the biology and data science involved in the use of microarray technology in biological and clinical research. We discuss how laborious experimental protocols involved in obtaining this data used in laboratories could benefit from using simulations of the data. We discuss the approach used in the simulation engine from [7]. We use this simulation engine to generate a prediction tool in Power BI, a Microsoft, business intelligence tool for analytics and data visualization [22]. This tool could be used in any laboratory using micro-arrays to improve experimental design by comparing how predicted signal intensity compares to observed signal intensity. Signal intensity in micro-arrays is a proxy for level of gene expression in cells. We suggest further development avenues for the prediction tool