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.     

Fast internal dynamics in polyelectrolyte gels measured by dynamic light scattering

Summary Dynamic light scattering was used to investigate the dynamics of sodium poly(styrene sulfonate) and fully neutralized poly(acrylic acid) gels as a function of the degree of swelling and weight ratio of cross-linking agent. It was shown that the collective diffusion coefficient increases with increasing degree of swelling and that the diffusion coefficient shows stronger concentration dependence than predicted by scaling arguments. For gel samples measured at the swelling equilibrium, the diffusion coefficient increases with increasing gel concentration for both gel systems.     

Distributed self-stabilizing placement of replicated resources in emerging networks

Emerging large scale distributed networking systems, such as P2P file sharing systems, sensor networks, and ad hoc wireless networks, require replication of content, functionality, or configuration to enact or optimize communication tasks. The placement of these replicated resources can significantly impact performance. We present a novel self-stabilizing, fully distributed, asynchronous, scalable protocol that can be used to place replicated resources such that each node is "close" to some copy of any object. We describe our protocol in the context of a graph with colored nodes, where a node's color indicates the replica/task that it is assigned. Our combination of theoretical results and simulation prove stabilization of the protocol, and evaluate its performance in the context of convergence time, message transmissions, and color distance. Our results show that the protocol generates colorings that are close to the optimal under a set of metrics, making such a protocol ideal for emerging networking systems.     

Using classification techniques for informal requirements in the requirements analysis-supporting system

In order to efficiently develop large-scale and complicated software, it is important for system engineers to correctly understand users’ requirements. Most requirements in large-scale projects are collected from various stakeholders located in various regions, and they are generally written in natural language. Therefore, the initial collected requirements must be classified into various topics prior to analysis phases in order to be usable as input in several requirements analysis methods. If this classification process is manually done by analysts, it becomes a time-consuming task. To solve this problem, we propose a new bootstrapping method which can automatically classify requirements sentences into each topic category using only topic words as the representative of the analysts’ views. The proposed method is verified through experiments using two requirements data sets: one written in English and the other in Korean. The significant performances were achieved in the experiments: the 84.28 and 87.91 F1 scores for the English and Korean data sets, respectively. As a result, the proposed method can provide an effective function for an Internet-based requirements analysis-supporting system so as to efficiently gather and analyze requirements from various and distributed stakeholders by using the Internet.     

Toughening mechanism for a rubber-toughened epoxy resin with rubber/matrix interfacial modification

For a rubber-toughened piperidine-DGEBA epoxy resin, the interface between the rubber particle and the epoxy resin matrix was modified by an epoxide end-capped carboxyl terminated butadiene and acrylonitrile random copolymer (CTBN). The end-capping epoxides used were a rigid diglycidyl ether of bisphenol-A (Epon 828), a short-chain flexible diglycidyl ether of propylene glycol (DER 736), and a long-chain flexible diglycidyl ether of propylene glycol (DER 732). The microstructures and the fracture behaviour of these rubber-modified epoxy resins were studied by transmission electron microscopy and scanning electron microscopy. Their thermal and mechanical properties were also investigated. In the rubber-modified epoxy resins, if the added CTBNs were end-capped by a flexible diglycidyl ether of propylene glycol (DER 732 or DER 736) before curing, the interfacial zone of the undeformed rubber particle, the degree of cavitation of the cavitated rubber particle on the fracture surface and the fracture energy of the toughened epoxy resin were all significantly increased. The toughening mechanism based on cavitation and localized shear yielding was considered and a mechanism for the interaction between cavitation and localized shear yielding that accounts for all the observed characteristics is proposed.     

Modeling of Temperature–Frequency Correlation Using Combined Principal Component Analysis and Support Vector Regression Technique

A good understanding of environmental effects on structural modal properties is essential for reliable performance of vibration-based damage diagnosis methods. In this paper, a method of combining principal component analysis (PCA) and support vector regression (SVR) technique is proposed for modeling temperature-caused variability of modal frequencies for structures instrumented with long-term monitoring systems. PCA is first applied to extract principal components from the measured temperatures for dimensionality reduction. The predominant feature vectors in conjunction with the measured modal frequencies are then fed into a support vector algorithm to formulate regression models that may take into account thermal inertia effect. The research is focused on proper selection of the hyperparameters to obtain SVR models with good generalization performance. A grid search method with cross validation and a heuristic method are utilized for determining the optimal values of SVR hyperparameters. The proposed method is compared with the method directly using measurement data to train SVR models and the multivariate linear regression (MLR) method through the use of long-term measurement data from a cable-stayed bridge. It is shown that PCA-compressed features make the training and validation of SVR models more efficient in both model accuracy and computational costs, and the formulated SVR model performs much better than the MLR model in generalization performance. When continuously measured data is available, the SVR model formulated taking into account thermal inertia effect can achieve more accurate prediction than that without considering thermal inertia effect.     

A new method for seed oil body purification and examination of oil body integrity following germination

Plant seeds store triacylglycerols as energy sources for germination and postgerminative growth of seedlings. The triacylglycerols are preserved in small, discrete, intracellular organelles called oil bodies. A new method was developed to purify seed oil bodies. The method included extraction, flotation by centrifugation, detergent washing, ionic elution, treatment with a chaotropic agent, and integrity testing by use of hexane. These processes subsequently removed non-specifically associated or trapped proteins within the oil bodies. Oil bodies purified by this method maintained their integrity and displayed electrostatic repulsion and steric hindrance on their surface. Compared with the previous procedure, this method allowed higher purification of oil bodies, as demonstrated by SDS-PAGE using five species of oilseeds. Oil bodies purified from sesame were further analyzed by two-dimensional gel electrophoresis and revealed two potential oleosin isoforms. The integrity of oil bodies in germinating sesame seedlings was examined by hexane extraction. Our results indicated that consumption of triacylglycerols reduced gradually the total amount of oil bodies in seedlings, whereas no alteration was observed in the integrity of remaining oil bodies. This observation implies that oil bodies in germinating seeds are not degraded simultaneously. It is suggested that glyoxisomes, with the assistance of mitochondria, fuse and digest oil bodies one at a time, while the remaining oil bodies are preserved intact during the whole period of germination.     

Effects of serotonin and the serotonin blocker metergoline on meal patterns and macronutrient selection

Serotonin [5-hydroxytryptamine(5-HT)] in the paraventricular nucleus (PVN) of rats has a suppressive effect on feeding behavior and causes a selective decrease in carbohydrate ingestion, specifically at the onset of the natural (dark) feeding period. Studies conducted here provide further evidence for this phenomena, showing a similar dose-related decrease in carbohydrate ingestion at dark onset after PVN injection of 5-HT or of the agonists, d-norfenfluramine or fluoxetine, which act through endogenous 5-HT. To further characterize the effects of this indoleamine on the macrostructure of feeding, a computer-automated data acquisition system was used to analyze macronutrient feeding patterns in freely feeding animals maintained on the pure diets of protein, carbohydrate, and fat. Results indicate that PVN administration of 5-HT at dark onset decreases intake of the carbohydrate nutrient by decreasing meal size, feeding time, and feeding rate for this nutrient and increasing the satiating effect of carbohydrate. These effects, which occur specifically during the first meal after injection, are opposite those seen after peripheral administration of the 5-HT receptor antagonist, metergoline. This drug stimulates feeding through a selective increase in carbohydrate intake, characterized by an increase in meal size, percent composition, and feeding time for this nutrient and a decrease in the satiety ratio for carbohydrate. These results implicate the serotonergic system in the termination of carbohydrate-rich meals that are prevalent during the early hours of the natural feeding cycle.