The effect of Pseudomonas aeruginosa infection on clinical parameters in steady-state bronchiectasis

STUDY OBJECTIVE: To investigate the effect of Pseudomonas aeruginosa infection on clinical parameters in Chinese patients with noncystic fibrosis and steady-state bronchiectasis. DESIGN: Prospective, cross-sectional clinicomicrobiological study with informed consent. SETTING: Consecutive outpatient recruitment from a specialist bronchiectasis respiratory clinic. PATIENTS: Outpatients (n = 100; 62 women; 55.1+/-16.7 years old; FEV1/FVC 1.4+/-0.7/2.1+/-0.9 L), who had stable respiratory symptoms for more than 3 weeks. MEASUREMENTS AND RESULTS: Respiratory pathogens isolated from the sputum were: Pseudomonas aeruginosa (33), Haemophilus influenzae (10), Moraxella catarrhalis (2), other Gram-negative bacilli (5), Streptococcus pneumoniae (6), Staphylococcus aureus (5), mycobacteria (3), and yeast (1). Clinical parameters in patients with positive isolation of P aeruginosa were compared with those without the organism in the sputum culture (non-P aeruginosa). In the P aeruginosa group, the FEV1/FVC ratio and sputum volume were lower (p < 0.005) and higher (p < 0.0001), respectively, than those of the non-P aeruginosa group. The FEV1/FVC ratio (< 60%) and sputum volume (grading > 5) were independently associated with a positive sputum isolation of P aeruginosa with odds ratios of 3.1 (confidence interval [CI] 1.2 to 8.4; p < 0.01) and 4.7 (CI 1.6 to 13.3; p < 0.001), respectively. CONCLUSIONS: P aeruginosa is the predominant respiratory pathogen isolated in the sputum of Chinese patients with steady-state bronchiectasis, and its isolation is associated with high sputum output (> or = 75th quartile) and moderately severe airflow obstruction (FEV1/FVC < 60%).     

Structural stability of single-layered LaNi4.25Al0.75 film and its electrochemical hydrogen-storage properties

AB5-based hydrogen storage thin fdms （LaNi4.25Al0.75）, deposited on Cu substrate by dc magnetron sputtering were investigated in this study. X-ray diffraction （XRD） revealed that the microstructure of the layer was in crystal form. SEM and AFM analyses proved that the film appeared to be rather rough with numerous randomly sized pores of approximately 15-40 in nm diameter. Structural stability of the film was examined by the combined analyses of DSC, XRD, and SEM, which indicated that this film maintained its structural stability below 500 K or so, and a network structure was observed on the film after being heated at 700 K for 30 min. Electrochemical hydrogen-storage properties of the films were investigated by simulated battery tests. It was found that single-layered LaNi4.25A10.75 film exhibited electrochemical hydrogen-storage properties similar to typical AB5 alloys in bulk, and the maximum discharge capacity of the film was about 220 mAh/g. After 20 charge/discharge cycles, small needle-shaped aluminium oxide was formed on some fractions of the film surface.     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

Service bid comparisons by fuzzy ranking in open railway market timetabling

In an open railway access market, the Infrastructure Provider (IP), upon the receipts of service bids from the Train Service Providers (TSPs), assigns track access rights according to its own business objectives and the merits of the bids; and produces the train service timetable through negotiations. In practice, IP chooses to negotiate with the TSPs one by one in such a sequence that IP optimizes its objectives. The TSP bids are usually very complicated, containing a large number of parameters in different natures. It is a difficult task even for an expert to give a priority sequence for negotiations from the contents of the bids. This study proposes the application of fuzzy ranking method to compare and prioritize the TSP bids in order to produce a negotiation sequence. The results of this study allow investigations on the behaviors of the stakeholders in bid preparation and negotiation, as well as evaluation of service quality in the open railway market.     

A mixture of generalized latent variable models for mixed mode and heterogeneous data

In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented.     

Using surface variability characteristics for segmentation of deformable 3D objects with application to piecewise statistical deformable model

To cope with the small sample size problem in the construction of Statistical Deformable Models (SDM), this paper proposes two novel measures that quantify the similarity of the variability characteristics among deforming 3D meshes. These measures are used as the basis of our proposed technique for partitioning a 3D mesh for the construction of piecewise SDM in a divide-and-conquer strategy. Specifically, the surface variability information is extracted by performing a global principal component analysis on the set of sample meshes. An iterative face clustering algorithm is developed for segmenting a mesh that favors grouping triangular faces having similar variability characteristics into a same mesh component. We apply the proposed mesh segmentation algorithm to the construction of piecewise SDM and evaluate the representational ability of the resulting piecewise SDM through the reconstruction of unseen meshes. Experimental results show that our approach outperforms several state-of-the-art methods in terms of the representational ability of the resulting piecewise SDM as evaluated by the reconstruction accuracy.     

Optimized centre-feed clarifier design for the Tai Po Sewage Treatment Works,Hong Kong

Stability of sludge blanket is a critical factor controlling the performance of clarifiers. A three-dimensional computational fluid dynamics (CFD) model was applied to optimize the clarifier design at the Tai Po Sewage Treatment Works, Hong Kong. Validated by field data, the CFD model evaluated the key clarifier design elements including side-water depth, centre-feed inlet, flocculation well and returned activated sludge (RAS) flow. Based on field observations and modelling results, the new design aims at eliminating the strong turbulence typical of centre-feed inlets in the clarifier, thus creating a better flocculation environment, and reducing disturbance to the sludge blanket. The modelling results also demonstrated a marked improvement in clarifier performance after the increase in clarifier depth and hence sludge storage capacity. The increased storage capacity reduces the risk of high sludge blanket levels upsetting the clarified effluent quality under high flow or solids loading conditions.