Reduction in photosystem II efficiency during a virus-controlled Emiliania huxleyi bloom

During viral infection of Emiliania huxleyi, laboratory studies have shown that photo-system (PS) II efficiency declines during the days post-infection and is thought to be associated with viral-induced interruption of electron transport rates between photosystems. However,measuring the impact of viral infection on PSII function in E. huxleyi populations from natural,taxonomically diverse phytoplankton communities is difficult, and whether this phenomenon occurs in nature is presently unknown. Here, chlorophyll fluorescence analysis was used to assess changes in PSII efficiency throughout an E. huxleyi bloom during a mesocosm experiment off the coast of Norway. Specifically, we aimed to determine whether a measurable suppression of the efficiency of PSII photochemistry could be observed due to viral infection of the natural E. huxleyi populations. During the major infection period prior to bloom collapse, there was a significant reduction in PSII efficiency with an average decrease in maximum PSII photochemical efficiency (Fv/Fm) of 17% and a corresponding 75% increase in maximum PSII effective absorption cross section(σPSII); this was concurrent with a significant decrease in E. huxleyi growth rates and an increase in E. huxleyi virus (EhV) production. As E. huxleyi populations dominated the phytoplankton community and potentially contributed up to 100% of the chlorophyll a pool, we believe that the variable chlorophyll fluorescence signal measured during this period was derived predominantly from E. huxleyi and, thus, reflects changes occurring within E. huxleyi cells. This is the first demonstration of suppression of PSII photochemistry occurring during viral infection of natural coccolithophore populations.

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.