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To investigate determinants of medication use among multi-ethnic pregnant women.A total of 641 pregnant women participated in this cross-sectional study in a Brussels university hospital. A questionnaire was used to obtain data on socio-demographic characteristics and medication use. Chi-squared tests and binary logistic regression analyses were performed using SPSS 19.Medication use during pregnancy (37%) was positively associated with age older than 35 years, Western origin, being born in Belgium, high education and employment status. Highly educated Western women had a greater exposure to drugs with an unknown safety profile than Arab/Turkish and 'Other (non-Western) origins' women. In the latter two groups, low education and nulliparity were the most important determinants of lower drug use. Nulliparous Arab/Turkish women used significantly less medications (17%) during pregnancy than parous women with the same ethnic background (34%; p = 0.024).Medication use during pregnancy is considerable but differs according to ethnicity. Age, parity, educational level, occupational status and duration of stay in Belgium are important determinants that should be taken into account for risk assessment and preventive measures targeting pregnant women.

Distributed lag models (DLM) are regression models that include multiple lagged exposure variables as covariates. They are frequently used to model the relationship between daily mortality and short-term air pollution exposures. Specifying a maximum lag number is but one of the difficulties in using a DLM for environmental epidemiology. We propose an easily extendible ensemble post-processing approach. The resultant estimates are both more parsimonious, approaching zero with increasing lag, and more efficient. The benefits are shown to be robust under various simulation scenario’s and illustrated with data from the National Morbidity, Mortality and Air Pollution Study.

Distributed lag models (DLM) are attractive methods for dealing with mortality displacement, however their estimates can have substantial bias when the data is generated by a multi-state model. In particular DLMs are not valid for mortality displacement. Alternative methods are scarce and lack feasibility and validation. We investigate the breakdown of DLM in three state models by means of simulation and propose simulation enhanced distributed lag models (SEDLM) to overcome the defects. The new method provides simultaneous estimates of the net effect (entry) and the displacement effect (exit). These have improved performance over the singular estimate from a regular DLM. SEDLM entry estimates have negligible bias and their variance is reduced. The exit estimates are unbiased and their variance is one order of magnitude lower with respect to the entry estimates. Applying SEDLM to the original Chicago data, the 95% highest posterior density intervals for both entry and exit contain 0, providing neither evidence for a ‘displacement effect’ nor for a ‘net effect’. Electronic supplementary material The online version of this article (doi:10.1186/s40064-016-3566-6) contains supplementary material, which is available to authorized users.

Background: We investigated the associations between daily sales of respiratory medication and air pollutants for various age groups in the Brussels-Capital Region (about one million inhabitants). Methods: Daily average concentrations of PM10, and NO2 at a resolution 4x4km were derived by interpolation from monitoring stations. These data was combined with reimbursement data of prescribed asthma and COPD medication from the Belgian social security for years 2005 to 2011. In this time-series study, we used over-dispersed Poisson Generalized Linear Models adjusted for daily minimum temperature, relative humidity, day of the week and influenza. We obtained up-to-three-week cumulative risk ratios (RR) for interquartile range (IQR) of exposure and their 95% confidence intervals (CI) for all ages and seven age groups. Findings: Median daily concentrations of PM10 and NO2 were 25µg/m3 (IQR=17·1) and 38µg/m3 (IQR=20·5), respectively. PM10 was associated with daily medication sales among individuals aged 13 to 64 years. For NO2, significant associations were observed among all age groups except >84 years old. The strongest RR were for NO2, among adolescents, including three weeks lags (RR=1.187 95%CI: 1.097-1.285). Interpretation: Daily concentrations of PM10 and NO2 were associated with increased sales of asthma and COPD medication in Brussels. For NO2, these associations were already established among pre-school age children and evident up to the young elderly (

Introduction The adverse health effects of exposure to air pollution have been well-established and include mortality, hospital admissions, emergency department visits, etc, but also less severe outcomes such as medication use and purchase. The economic impact, an additional motivator for policy, has been studied primarily for the more severe outcomes. Methods Purchase data of reimbursed medications typically prescribed for asthma and chronic obstructive pulmonary disease, were obtained through the mandatory Belgian health insurance system. A time series analyses approach was used to model daily sales on daily air pollution concentrations (NO 2 , PM 10 and PM 2.5 ) for residents of the Brussels Capital Region as a whole. In addition, a higher geographical resolution of both sales and pollutant concentrations allowed for a multi-sector approach. Annual savings were estimated for the scenario of a 10% reduction in each of the pollutants. Results Medication purchase was significantly associated with NO 2 concentrations, leading to an annual cost saving potential of € 107,845 [95%CI: € 71,483–€ 143,823] in R03 sales (WHO classification for drugs of obstructive airway diseases). Saving potentials of PM 10 and PM 2.5 were not significant. Estimates were not sensitive to the geographical resolution, however, higher precision can be obtained with higher resolution data, subject to the condition that the number of sales is sufficiently large.

The use of chromatographic descriptors as alternative for Caco-2 permeability in drug absorption screening was evaluated. Therefore, retentions were measured on 17 Reversed-Phase Liquid Chromatographic systems, considered to be orthogonal or dissimilar, and an Immobilized Artificial Membrane (IAM) system. Retentions on a Micellar Liquid Chromatography system were taken from the literature. From this set of systems, those found dissimilar for the used data set were selected. The retention factors on these systems were then used as descriptors in QSAR modelling. Modelling was performed using Stepwise Multiple Linear Regression. This resulted in a model using only two chromatographic systems with good descriptive and acceptable predictive properties. A high qualitative model was obtained by combining both chromatographic systems selected in the previous model with a lipophilicity parameter (the squared Moriguchi n-octanol/water partition coefficient) and the molecular volume.

Introduction The aim of study is comparing the haemostatic properties of conventional monopolar resection (TURP) and bipolar transurethral resection in saline (TURIS) of the prostate in patients under chronic oral anticoagulants. Material and methods Out of a cohort group of 550 endoscopic resections for bladder outlet obstruction, 176 patients on chronic oral anticoagulant therapy required endoscopic resection either by monopolar TURP or bipolar TURIS technology. Changes in haemoglobin, blood transfusion, and clot retention were compared between both groups. Results Mean postoperative change in haemoglobin level was -1.21 ±0.92 mg/dl in the TURP group compared to -1.29 ±0.99 mg/dl in the TURIS group (p = 0.603). The need for blood transfusions and the mean numbers of units transfused did not significantly differ between the 2 groups. Clot retention appeared in 12 patients (15%) in the TURP group compared to 13 patients (13%) in the TURIS group (p = 0.828). Conclusions Despite promising experimental results of better haemostasis and deeper coagulation depth, bipolar technology does not permit one to reduce the amount of blood loss when compared to patients treated by conventional monopolar technology in this study group of patients on oral anticoagulation therapy. Patients on oral anticoagulants suffer more incidents of clot retention, which sometimes results in re-hospitalisation.

Background: There is little information on the decision-making process for single implant treatment in general practice. Aim: To study the incidence of and the factors associated with the decision to perform single implant treatment after tooth extraction by general practitioners in a private, fee-for-service setting. Methods: One hundred practitioners with a general dental practice in Ghent were randomly selected from an official list received by the Belgian Social Security Institute. Clinicians were asked to fill in a study form for every single extraction they performed during an 8-week period. The study form related to the treatment decision as discussed with the patient and a number of patient- and clinician-related factors. The association of these factors with single implant treatment was evaluated using univariate tests and logistic regression. A decision-tree was also constructed with the predictors from the regression analysis as independent variables. Results: Ninety-four general dentists (52 males, 42 females; mean age 49; range 24–68)agreed to participate and extracted 1180 single teeth in an equal number of patients (50% males, 50% females; mean age 53; range 18–90). The main reasons for tooth loss were caries (48%) and periodontal disease (28%). At the time of extraction tooth replacement was deemed necessary in half of the patients and are movable partial denture was chosen in 55% of them. Similar frequencies were found for fixed partial denture (23%) and single implant treatment (21%). Although the vast majority of patient- and clinician-related factors showed a significant association with the latter on the basis of univariate tests, logistic regression only identified seven predictors. These included location of the extracted tooth, number of missing teeth, regular supportive care, bone loss at adjacent teeth, restoration level of adjacent teeth, gender of the clinician and dentists’ experience in implant prosthetics. The decision tree identified bone loss at adjacent teeth and number of missing teeth as the most important predictors for single implant treatment. Conclusions and clinical implications: If tooth replacement was deemed necessary at the time of extraction, a single implant was the treatment of choice in only one-fifth of the patients. Mainly oral factors had an impact on the decision-making process in contrast to patients’ background and medical factors. Dentists’ experience in implant prosthetics also showed a positive association with single implant treatment as opposed to dentists’ experience in implant surgery.

Multiple adaptive discrete wavelet transforms were applied during a multiple regression of spectroscopic data for the purpose of investigating the hypothesis — does the use of different wavelets, at different points, within a spectrum, elucidate predictive capability. The model investigated was a constrained stacking regression ensemble with individual regression models chosen initially by a Bayes Metropolis search. The ensemble approach provided the ability to combine different regression models that used different types of wavelets. Models were applied to a publically available dataset, pertaining to biscuit dough, of near infrared spectra, that were measured by a FOSS 5000, and laboratory measurements of the fat, flour, sugar and moisture content. The resultant model, which is referred to as a joint multiple adaptive wavelet regression ensemble (JMAWRE), was found to be the superior predictive model when compared to models that used standard wavelets as part of the regression ensembles. The JMAWRE was also superior when compared to other models from literature that used the same publicly available NIR dataset.

BACKGROUND: Infant death rate has declined over the past decades, yet remains high in Palestine. The topic of infant death is well researched and for the first time in Palestine. The objective of our study is to assess the level of awareness and health behaviour of mothers and how this behaviour has affected infant mortality. METHODS: Person to person interviews were done with 550 mothers of infants (275 cases and 275 controls) in the Gaza Strip. Stillbirths were excluded. Binary logistic regression analyses were used to identify the relationship of health behavioural factors and infant mortality. RESULT: The study showed that infant mortality risks were higher in mothers exposed to passive smoking. Infant mortality was lower for infants receiving exclusive breastfeeding. There was also a significant association between the sleeping position of the baby and infant mortality. CONCLUSION: The findings underscore the importance of explicit attention to health education. A well-organized consultation and health promotion approach should focus on couples whose child has died, in order to combat infant mortality.

INTRODUCTION: The aim was to evaluate the postoperative morbidity and outcome of palliative endoscopic resections for relief of infravesical obstruction in prostate cancer patients with hormone deprivation therapy, and to investigate the added value of bipolar technology over conventional monopolar resections. MATERIAL AND METHODS: A retrospective study was performed on 70 patients with prostate cancer under hormone deprivation therapy undergoing 75 endoscopic procedures, by either monopolar or bipolar technology, between August 2005 and March 2009 at a single institution. The analysis used outpatient, inpatient, and operative records, and observations of electrolyte changes in the serum, postoperative morbidity, and the overall results of palliative endoscopic resections. Preoperative cancer stages and grades were compared with the pathological findings after surgery. Postoperative outcome and complications of conventional monopolar and bipolar technology were compared. RESULTS: Over a period of 44months, 34 conventional monopolar resections were performed in 32 patients and 41 bipolar resections in 38 patients. Patients' profiles regarding age, initial cancer stage and grade, resection weight, resection speed, catheterization time, and hospital stay were similar in both groups. No statistically significant difference was observed in sodium drop (p = 0.802), clot retention (p = 0.565), or urinary retention (p = 0.292). The overall success rate in relieving obstruction leading to spontaneous voiding was 77%. While 38% of the patients had a high grade tumour at diagnosis, 79% were found to be high grade after the endoscopic resection (p

A new non hierarchical clustering procedure is used to evaluate an existing classification of iron meteorites. It is shown that the classification obtained resembles the existing classification to a large degree. This confirms the consistency of both the clustering method and the existing classification. The principal disagreement between the existing classification and the clustering concerns the so-called anomalous meteorites. The reason for this disagreement is discussed.

The objective of this study was to compare the efficacy and safety of bipolar transurethral resection in saline (TURIS) and conventional monopolar transurethral resection (TURP) in men with voiding difficulties due to high volume (>60g) benign prostatic hyperplasia. Between May 2005 and January 2009, 66 men with bladder outlet obstruction due to large benign hyperplasia (BPH) (>60 g) underwent transurethral resection, either by Olympus monopolar or Olympus bipolar TURIS technique. Perioperative and outcome data were recorded and compared. There was no significant difference in patient age, prostate size, PSA, uroflow rate, post voiding residual urine, operation time, catheterization time, and hospital stay. In the monopolar TURP group, an important drop in serum sodium was observed (3.12 mmol/L), statistically significantly different (p = 0.012) from the 1.30 mmol/L drop in the bipolar TURIS group. One case of clinical TUR syndrome was observed in the conventional monopolar TURP group. Early and late complications such as clot retention, urinary retention, bladder neck stenosis, and urethral stricture were identical in both groups. The bipolar TURIS device is a valid treatment option for patients with BPH-related voiding disorders due to high volume BPH. Obviating the risk of TUR syndrome, this minimally invasive procedure may be a good surgical option in the urologist's armamentarium.

Objective: We have previously demonstrated high concentrations of the glycoprotein osteoprotegerin (OPG) in biopsies of abdominal aortic aneurysm (AAA), and demonstrated that ligation of the nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) downregulates OPG in vitro and within a mouse model. The aims of this study were to assess the associations between circulating concentrations of OPG, polymorphisms of the gene encoding PPARγ (PPARG), AAA presence and growth. Design, patients and measurements: Two genetic polymorphisms in PPARG were assessed in 4227 men, 699 of whom had an AAA. For 631 men, who had AAAs, maximum aortic diameter was monitored by yearly ultrasound for a median of 5 years. Plasma OPG was measured in 838 men, 318 of whom had an AAA. Results: Plasma concentrations of OPG were independently associated with AAA (adjusted odds ratio 1.38, 95% CI 1.10–1.72). The PPARG c.1347C > T polymorphism was associated with plasma concentrations of OPG (beta 0.12, P C polymorphism was weakly associated with AAA (adjusted odds ratio 1.28, 95% CI 1.01–1.61). PPARG c.1347C > T was associated with increased AAA growth (recessive model, P = 0.03). Conclusions: Circulating concentrations of osteoprotegerin are associated with abdominal aortic aneurysm and with one peroxisome proliferator-activated receptor gamma gene polymorphism. Peroxisome proliferator-activated receptor gamma gene polymorphisms are weakly associated with abdominal aortic aneurysm presence and growth. Confirmation of these findings is required in other cohorts.

A quantitative structure-activity relationship (QSAR) model is typically developed to predict the biochemical activity of untested compounds from the compounds' molecular structures. "The gold standard" of model validation is the blindfold prediction when the model's predictive power is assessed from how well the model predicts the activity values of compounds that were not considered in any way during the model development/calibration. However, during the development of a QSAR model, it is necessary to obtain some indication of the model's predictive power. This is often done by some form of cross-validation (CV). In this study, the concepts of the predictive power and fitting ability of a multiple linear regression (MLR) QSAR model were examined in the CV context allowing for the presence of outliers. Commonly used predictive power and fitting ability statistics were assessed via Monte Carlo cross-validation when applied to percent human intestinal absorption, blood-brain partition coefficient, and toxicity values of saxitoxin QSAR data sets, as well as three known benchmark data sets with known outlier contamination. It was found that (1) a robust version of MLR should always be preferred over the ordinary-least-squares MLR, regardless of the degree of outlier contamination and that (2) the model's predictive power should only be assessed via robust statistics. The Matlab and java source code used in this study is freely available from the QSAR-BENCH section of www.dmitrykonovalov.org for academic use. The Web site also contains the java-based QSAR-BENCH program, which could be run online via java's Web Start technology (supporting Windows, Mac OSX, Linux/Unix) to reproduce most of the reported results or apply the reported procedures to other data sets.

The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

Two new approaches, boosted regression trees (BRTs) and two-step BRT, were evaluated for modelling and predicting the blood-brain barrier (BBB) passage of drugs. Classification and regression trees (CART) were used as a base learner in BRT. In two-step BRT, a linear model (stepwise multiple linear regression (MLR) or partial least squares (PLS)) was built first, then BRT was applied to model the residuals of the linear model and both models were added. Both approaches were compared with the CART, MLR and PLS models. It was observed that BRT could improve the descriptive and predictive abilities compared to a single CART and that the stepwise MLR-BRT results in slightly improved descriptive and predictive properties compared to the MLR model. The combination of PLS and BRT did not result in an improvement, compared to the individual PLS model. The best models were obtained with stepwise MLR-BRT and PLS. It was shown that the combination of linear models with BRT is an approach that has potential and can be considered for future QSAR modelling.

Techniques that provide a rapid and widespread assessment of crop properties equip industry decision makers with knowledge to improve their farming environment, both tactically and strategically. An interdisciplinary approach that links the fields of hyperspectral remote sensing, statistical data mining and sugarcane systems was undertaken to establish new relationships to determine variety type and crop age of sugarcane plants. In contrast to commonly used sensors such as those occupied by Landsat satellites, images captured by hyperspectral sensors can provide a more detailed assessment of crop status. Appropriate statistical analysis methods are needed to decode the multifaceted information recorded in these hyperspectral images. A range of statistical approaches have been applied for analysis of an EO-1 hyperion hyperspectral image from a major sugarcane growing region in Australia. Two relatively new classification methods - support vector machines and random forests - demonstrated superior performance in classifying sugarcane variety and crop cycle, e.g. the number of times that the plant has grown back after harvest, when compared against traditional statistical methods. Assignment results were further enhanced when classifications of pixels within sugarcane paddocks were aggregated to paddock classifications using paddock boundary information. Whilst the analysis methods of the hyperspectral data have been tested for the classification of variety and crop cycle, the potential application arenas for this type of imagery is both extensive and relatively unexplored. This type of data coupled with appropriate analysis methods will play a vital role in futuristic sustainable agriculture practices as this imagery becomes more accessible and as land managers and researchers become more aware of the types of decisions that hyperspectral remote sensing data can aid.

An ensemble of regression models predicts by taking a weighted average of the predictions made by individual models. Calculating the weights such that they reflect the accuracy of individual models (post processing the ensemble) has been shown to increase the ensemble's accuracy. However, post processing cluster ensembles has not received as much attention because of the inherent difficulty in assessing the accuracy of an individual cluster model. By enforcing the notion that clusters must be predictable, this paper suggests a means of implicitly post processing cluster ensembles by drawing analogies with regression post processing techniques. The product of the post processing procedure is an intelligently weighted co-occurrence matrix. A new technique, similarity-based k-means (SBK), is developed to split this matrix into clusters. The results using three real life datasets underpinned by chemical and biological phenomena show that splitting an intelligently weighted co-occurrence matrix gives accuracy that approaches supervised classification methods.

Objective: To investigate early changes in leukocyte subsets and autonomic function as predictors of the development of poststroke infections.Methods: We assessed the time course of leukocyte subsets in the blood of 59 patients with acute ischemic stroke. We divided the patients into 2 groups: those who developed infections during the first 7 days after stroke onset and those who did not. We measured urinary norepinephrine and epinephrine concentrations and pulse rate variability indices within 24 hours of admission.Results: We found that the number of circulating natural killer (NK) cells within the first hours after stroke was higher in stroke patients who developed infections (mean 435 cells/mL; 95% confidence interval [CI] 321-588) than in stroke patients who did not develop infections (mean 236 cells/mL; 95% CI 186-300; p = 0.001). This was followed by a decrease in all lymphocyte subsets from admission to day 1, varying between 22% and 40%, which was not seen in patients without poststroke infection (mean increase varied between 2% and 23%; all p Conclusions: High circulating NK cell count within the first hours after ischemic stroke onset followed by a drop in all lymphocyte subsets identified patients who developed infections and may be caused by a sympathovagal imbalance with sympathetic overweight. These findings need to be validated in larger studies.

The objective of this study was to investigate the effects of some commonly used sample preparation procedures, including overnight freezing, and the type of homogeniser on the near-infrared (NIR) spectra of red grape homogenates. Homogenates (n = 284) of three red grape varieties were prepared using one of three types of homogenisers after one of two types of short term storage (fresh and overnight freezing) and then scanned in a FOSS NIRSystems6500 instrument (400–2500 nm). The NIR spectral data were then analysed using various discrimination techniques, namely Penalized Discriminant Analysis (PDA), Multivariate Adaptive Regression Splines discriminant analysis (MARS-DA) and Random Forests (RF) yielding correct classification rates (CCR) of 63.4%, 58.6% and 45.6%, respectively. To improve the CCR of the discrimination models, feature extraction from the NIR spectral data was performed using an adaptive discrete wavelet transformation (DWT). The DWT algorithm employs an adaptive wavelet basis function that maximizes the discrimination between the different combinations of homogenisers, storage and grape varieties. The results after adaptive DWT on the NIR spectra resulted in CCRs of 99.93%, 99.2% and 76.4% for PDA, MARS-DA and RF, respectively. Further analysis of adaptive DWT PDA via MANOVA revealed significant differences in the main and interaction effects of the three treatments, which were then associated with specific regions within the NIR spectrum.

Wavelet based analysis for mass spectrometry (MS) profiles of three groups of patients are analyzed for the purpose of developing a classification model. The first step in our model uses a DWT for feature extraction, using a linear combination of Symlets, Daubechies and Coiflets wavelet bases – collectively known as a super wavelet. Random Forests and Treeboost are then used to analyze the super wavelet coefficients to form the classification model. The method is illustrated using the publicly available prostate SELDI-TOF MS data from the American National Cancer Institute (NCI). The NCI data consists of 322 MS profiles with 15154 M / Z ratios, comprising of 69 malignant, 190 benign and 63 control patients, which we randomly divided into 70% training and 30% testing. From the Random Forest models, the super wavelet performed 2.7% to 5.7% better than other single wavelet types to give a 100% test set prediction rate for cancerous patients.

Multivariate Regression Trees, an intuitive and simple regression technique, intrinsically produce homogenous subsets of data. These characteristics imply that Multivariate Regression Trees have the potential to be utilised as an easily interpretable clustering method. The suitability of Multivariate Regression Trees as a clustering technique is investigated with two real datasets containing only explanatory variables. The preliminary results show that Multivariate Regression Trees as a clustering algorithm produce clusters of similar quality to the well-known K-means technique, and more recent approaches to Cluster Analysis including Mixture Models of Factor Analysers and Plaid Models. The study also evaluates the suitability of various criteria used to describe cluster solutions.

Multivariate adaptive regression splines (MARS) and a derived method two-step MARS (TMARS) were used for modelling the gastro-intestinal absorption of 140 drug-like molecules. The published absorption values for these molecules were used as response variable and calculated molecular descriptors as potential explanatory variables. Both methods were compared and their potential use in quantitative structure–activity relationship (QSAR) context evaluated. The predictive abilities of the models were studied using different sequences of Monte Carlo cross validation (MCCV). It was shown that both types of models had good predictive abilities and that for the data used, MARS gave better results than TMARS. It could be concluded that both methods could be valuable for QSAR modelling.

This manuscript shows the usefulness of Projection Pursuit (PP) and Multivariate Regression Trees (MRT) for analytical data exploration. Additionally, features of Projection Pursuit and kurtosis as a projection index are presented. The ability of Projection Pursuit to discover groups in the data is compared to classical Principal Component Analysis (PCA). Moreover, it is also demonstrated how the presence of groups in the data can be explained in terms of explanatory variables with the aid of Projection Pursuit and Multivariate Regression Trees. Neither Projection Pursuit nor Multivariate Regression Trees are commonly used for exploring chemical data however, they are able to enrich to a high extent the interpretation.

PURPOSE: We studied functional damage in the bladder after intravesical administration of mitomycin C or epirubicin in rats. MATERIALS AND METHODS: A total of 40 rats were divided into 4 groups. Rats in 3 groups received intravesical instillations of mitomycin C, epirubicin or saline. Rats in group 4 received no intravesical instillations and served as normal controls. The doses of drugs administered were based on those used for therapeutic purposes. Changes in vesical capacity and bladder wall compliance were determined. Compliance was defined as the change in vesical volume induced by a given change in pressure. RESULTS: Weekly instillations of 1 mg/ml mitomycin C caused a statistically significant decrease in vesical volume and compliance, which persisted 3 weeks after the cessation of therapy. Epirubicin also induced changes in vesical volume and compliance but only that in bladder compliance achieved statistical significance. These changes were not seen in NaCl treated animals or in normal controls. CONCLUSIONS: Repeat intravesical instillations of mitomycin C have a prolonged effect on bladder function that is not seen after saline solution or epirubicin instillation.

We introduce a new method of unsupervised cluster exploration and visualization for spectral datasets by integrating the wavelet transform, principal components and Gaussian mixture models. The Bayesian Information Criterion (BIC) and classification uncertainty performance criteria are used to guide an automated search of commonly available wavelets and adaptive wavelets. We demonstrate the effectiveness of the proposed method in elucidating and visualizing unsupervised clusters from near infrared (NIR) spectral datasets. The results show that informative feature extraction can be achieved through both commonly available wavelet bases and adaptive wavelets. However, the features from the adaptive wavelets are more favorable in conjunction with unsupervised Gaussian mixture models through a user specified internal linkage function.

Feature selection is a valuable technique in data analysis for information-preserving data reduction. This paper describes Classification and Regression Trees (CART) and Multivariate Regression Trees (MRT)-based approaches for both supervised and unsupervised feature selection. The well-known CART method allows to perform supervised feature selection by modeling one response variable (y) by some explanatory variables (x). The recently proposed CART extension, MRT can handle more than one response variable (y). This allows to perform a supervised feature selection in the presence of more than one response variable. For unsupervised feature selection, where no response variables are available, we propose Auto-Associative Multivariate Regression Trees (AAMRT) where the original variables (x) are not only used as explanatory variables (x), but also as response variables (y=x). Since (AA)MRT is grouping the objects into groups with similar response values by using explanatory variables, this means that the variables are found which are most responsible for the cluster structure in the data. We will demonstrate how these approaches can improve (the detection of) the cluster structure in data and how they can be used for knowledge discovery.

Climate is a key driver of sugarcane productivity. Advance knowledge of the likely climate and its impact on production could add value to production, harvest and marketing efforts. A climate forecast system that incorporates five patterns or phases of the southern oscillation index is used to assess whether an early indication of sugarcane yield anomalies in Australia can be produced. Results indicate that, for certain sugarcane growing regions, the climate forecast system offers better estimates of the direction of the anomaly when compared with no climate forecast system. Improved results of the direction of the yield anomaly can be obtained some 7 months prior to the commencement of harvest. This information can then be used by marketers to plan better the customer allocations, shipping schedules and storage requirements for the next season. Advance knowledge of the crop size can also assist industry decision makers in scheduling when the harvest season should commence. Further research is required to determine if other climatic indices, such as sea surface temperatures, can improve yield estimation. Consideration also needs to be given to determining whether the magnitude and the direction of the yield anomaly can be more accurately forecasted with varying lead times.

On October 4, 1992, the El Al Boeing crashed in the residential quarter ‘Bijlmermeer’ in Amsterdam (The Netherlands). In the years after the plane crash, local residents and assistance personnel began reporting a variety of unusual symptoms not unlike those reported by patients with chronic fatigue syndrome (CFS) and Gulf War Syndrome (GWS). The aim of this study was to define the symptom constellations reported by the patients and assess the possible causes of the illness. Standardized psychological questionnaires (MMPI-II, SCL-90, KPS and a complaints checklist) were used to screen for psychological changes and to describe the symptoms reported by the patients. Differences between local residents and assistance personnel, gender differences, Mycoplasma-infected and Mycoplasma non-infected patients were monitored. The major symptoms reported were extreme fatigue, non-restorative sleep, concentration-problems, memory problems and muscle and joint pains. There were no changes in the SCL-90 response...

The aim of this study was to determine if hyperbaric oxygen treatment (HBOT) could be used as adjunctive therapy and if HBOT could increase the quality of life in such a way that the functional status would improve in patients with an infection. A randomized, controlled trial was conducted on 15 Mycoplasma sp. infected CFS (CDC 1994) patients and 14 CFS (CDC 1994) patients with no evidence of a Mycoplasma infection were enrolled in a convenience randomization sample from our referral clinic. No statistical differences were found by use of univariate repeated measures although Bodily Pain as measured by the SF-36 seems to decrease after hyperbaric therapy (Greenhouse-Geisser: p = .010). Trends were found using paired t-testing for Mycoplasma infected CFS patients. The general perceived fatigue seemed to decrease after hyperbaric therapy (General Fatigue: p = .06). Directly after one week of hyperbaric therapy general fatigue improved (p = .03) but there was a reduction of activity (reduced activity: p = .05) and general perceived health (general health: p = .04). One month later the physical role increased (Role-Physical: p = .07). Although more data is required to make firm conclusions, trends were found. Reduced fatigue, increased levels of activity and an improved reaction time improved significantly their quality of life and therefore, enhanced also their functional status and thus could be used as an adjunctive therapy.

The deregulation of the 2,5A synthetase RNase L antiviral pathway and the prevalence of Mycoplasma spp. in subsets of Chronic Fatigue Syndrome (CFS) have been separately reported in the scientific literature. We hypothesised that a co-morbid pathophysiological mechanism involving infection by Mycoplasma spp. and the deregulation of the 2,5A synthetase/RNase L antiviral pathway may exist in CFS. Therefore, 186 consecutive CFS patients were enrolled. Mycoplasma detection was performed using forensic polymerase chain reaction. For RNase L determination, a radioactive probe was used to label 2,5A binding proteins in unfractionated peripheral blood mononuclear cell extracts. Myco-plasma-infected CFS patients presented with significantly elevated RNase L-ratio, compared to non-infected age- and sex-matched patients (p = 0.016). These results suggest that mycoplasma infections may cause deregulation of the 2,5A synthetase RNase L antiviral pathway in patients with CFS

An impaired immune system and opportunistic infections are considered important characteristics in the pathophysiology of Chronic Fatigue Syndrome (CFS). Using immunofluorescence we examined healthy subjects (N = 35) and two subsets of CFS patients: those without evidence of Mycoplasma (N = 55) and those with evidence of a Myco-plasma infection in their blood (N = 131). Using monoclonal antibodies and forensic polymerase chain reaction for detection of M. hominis, M. fermentans, M. pneumoniae and M. penetrans, we examined leukocytes in peripheral blood samples. Both patient groups presented with significantly elevated CD25+ (activated) cells as compared to healthy volunteers. CFS patients without evidence of mycoplasma infection(s) had increased amounts of CD5+ B-cells. Stepwise discriminant analysis indicated the number of activated cells, number of memory CD4+ cells and percentage of suppressor T-cells (lower in Mycoplasma+ patients as compared to Mycoplasma- patients) as the discriminant variab...

PURPOSE: To investigate a computed tomographic (CT) protocol with iterative reconstruction at conventional radiography dose levels for the assessment of structural lung abnormalities in patients with cystic fibrosis ( CF cystic fibrosis ). MATERIALS AND METHODS: In this institutional review board-approved study, 38 patients with CF cystic fibrosis (age range, 6-58 years; 21 patients 18 years) underwent investigative CT (at minimal exposure settings combined with iterative reconstruction) as a replacement of yearly follow-up posteroanterior chest radiography. Verbal informed consent was obtained from all patients or their parents. CT images were randomized and rated independently by two radiologists with use of the Bhalla scoring system. In addition, mosaic perfusion was evaluated. As reference, the previous available conventional chest CT scan was used. Differences in Bhalla scores were assessed with the χ(2) test and intraclass correlation coefficients ( ICC intraclass correlationcoefficient s). Radiation doses for CT and radiography were assessed for adults (>18 years) and children (

Ambient air pollution levels in Brussels (Belgium) remained relatively large throughout the last decade. In addition, few health effect estimates have been made available for this region. To invest...