EEG spectral abnormalities and psychosis as predictors of cognitive and functional decline in probable Alzheimer's disease

We examined whether either psychotic features (e.g., delusions and hallucinations) or EEG abnormalities are associated with more rapid progression of Alzheimer's disease (AD). AD patients with psychosis have exhibited more EEG abnormalities than those without psychosis, and both abnormal EEG and psychosis have been noted to be predictors of functional and cognitive decline in AD. Ninety-five probable AD patients participating in a longitudinal study of dementia had an EEG and a semistructured psychiatric interview at baseline. Using EEG spectral analysis, we classified records as normal/abnormal based on the parasagittal mean frequency. Patients with abnormal EEGs were more functionally (e.g., Blessed Rating Scale for activities of daily living) and cognitively (e.g., Mini-Mental State) impaired than patients with normal EEG. AD patients with psychosis were only more functionally impaired than patients without psychosis. A two-factor analysis showed no interaction between abnormal EEG and psychosis. In addition, using a Cox proportional hazard model adjusted for age and education, the presence of an abnormal EEG or psychotic symptom at study entry was associated with higher risk of reaching severe cognitive and functional impairment during follow-up. Neither abnormal EEG nor the presence of psychosis predicted death. These results indicate that both abnormal EEG and psychosis are independent predictors of disease progression but not of physical survival.     

Triple check for antigen specificity of B cells during germinal centre reactions

Somatic hypermutation of the immunoglobulin variable genes during germinal reactions might permit the expansion of B-cell clones with unwanted (e.g. autoreactive) specificity. Here, Ernst Lindhout and colleagues propose three antigen-specific checkpoints that ensure the appropriate antigen specificity of activated B cells is maintained by regulating the activation, selection and further differentiation of B cells.     

Flotation machine design: impeller—stator interaction

The separate contributions of impellers and stators to suction are studied by means of interaction models. From these, suction can be predicted when interchangeable components are used in any combination, and in either direction of impeller rotation. Methods are devised by which numerical values of the impeller and stator terms can be simply assigned. The resulting model provides good agreement with experiment. The influence of variability in component geometry is reflected in the model parameters. Possible applications of the model are suggested.     

Intelligent identification and control for autonomous guided vehicles using adaptive fuzzy-based algorithms

Fuzzy logic was first suggested as the mechanism by which humans drive cars. This paper addresses the use of fuzzy logic and algorithms towards the intelligent autonomous motion control of land vehicles. To cope with vehicle complexities, internal parametric changes, and with unpredictable environmental effects, the controllers that are presented, whilst heuristic in nature, are self-organizing or self-learning in that they generate automatically by observation an experiential rule base that models the vehicle, and via an appropriate performance index an optimal control rule base that is robust to large parametric changes. The methodology presented is applicable to any complex process which is too difficult to model or control using conventional methods, or which has relied on the experience of a human operator. An overview of fuzzy logic and static fuzzy logic control (akin to expert systems) is provided, together with illustrative examples.