Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models

Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, for the difference of the precision measures between two competing rules can then be constructed. All the proposals are illustrated with two real examples and their finite sample properties are evaluated via a simulation study.

Surface antigens and potential virulence factors from parasites detected by comparative genomics of perfect amino acid repeats

ABSTRACT: BACKGROUND: Many parasitic organisms, eukaryotes as well as bacteria, possess surface antigens with amino acid repeats. Making up the interface between host and pathogen such repetitive proteins may be virulence factors involved in immune evasion or cytoadherence. They find immunological applications in serodiagnostics and vaccine development. Here we use proteins which contain perfect repeats as a basis for comparative genomics between parasitic and free-living organisms. RESULTS: We have developed Reptile http://reptile.unibe.ch, a program for proteome-wide probabilistic description of perfect repeats in proteins. Parasite proteomes exhibited a large variance regarding the proportion of repeat-containing proteins. Interestingly, there was a good correlation between the percentage of highly repetitive proteins and mean protein length in parasite proteomes, but not at all in the proteomes of free-living eukaryotes. Reptile combined with programs for the prediction of transmembrane domains and GPI-anchoring resulted in an effective tool for in silico identification of potential surface antigens and virulence factors from parasites. CONCLUSION: Systemic surveys for perfect amino acid repeats allowed basic comparisons between free-living and parasitic organisms that were directly applicable to predict proteins of serological and parasitological importance. An on-line tool is available at http://genomics.unibe.ch/dora.

Toxic effects of brake wear particles on epithelial lung cells in vitro

ABSTRACT: BACKGROUND: Fine particulate matter originating from traffic correlates with increased morbidity and mortality. An important source of traffic particles is brake wear of cars which contributes up to 20% of the total traffic emissions. The aim of this study was to evaluate potential toxicological effects of human epithelial lung cells exposed to freshly generated brake wear particles. RESULTS: An exposure box was mounted around a car's braking system. Lung cells cultured at the air-liquid interface were then exposed to particles emitted from two typical braking behaviours ("full stop" and "normal deceleration"). The particle size distribution as well as the brake emission components like metals and carbons was measured on-line, and the particles deposited on grids for transmission electron microscopy were counted. The tight junction arrangement was observed by laser scanning microscopy. Cellular responses were assessed by measurement of lactate dehydrogenase (cytotoxicity), by investigating the production of reactive oxidative species and the release of the pro-inflammatory mediator interleukin-8. The tight junction protein occludin density decreased significantly (p < 0.05) with increasing concentrations of metals on the particles (iron, copper and manganese, which were all strongly correlated with each other). Occludin was also negatively correlated with the intensity of reactive oxidative species. The concentrations of interleukin-8 were significantly correlated with increasing organic carbon concentrations. No correlation was observed between occludin and interleukin-8, nor between reactive oxidative species and interleukin-8. CONCLUSION: These findings suggest that the metals on brake wear particles damage tight junctions with a mechanism involving oxidative stress. Brake wear particles also increase pro-inflammatory responses. However, this might be due to another mechanism than via oxidative stress.

First foot prints of chemistry on the shore of the Island of Superheavy Elements

Chemistry has arrived on the shore of the Island of Stability with the first chemical investigation of the superheavy elements Cn, 113, and 114. The results of three experimental series leading to first measured thermodynamic data and qualitatively evaluated chemical properties for these elements are described. An interesting volatile compound class has been observed in the on-line experiments for the elements Bi and Po. Hence, an exciting chemical study of their heavier transactinide homologues, elements 115 and 116 is suggested.

Pupillometry: the eyes shed fresh light on the mind

Recent studies provide promising methodological advances in the use of pupillometry as on-line measurement of cognitive processes and show that visual attention allocation, mind-wandering, mental imagery, and even rhyme expectations can influence the size of the human pupil.

Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^

Physical oceanography and current meter measurements on the Gulf of Lion continental margin between June 1993 and July 1996

Nine hydrographic cruises were performed on the Gulf of Lion continental margin between June 1993 and July 1996. These observations are analysed to quantify the fluxes of particulate matter and organic carbon transported along the slope by the Northern Current and to characterise their seasonal variability. Concentration of particulate matter and organic carbon are derived from light-transmission data and water sample analyses. The circulation is estimated from the geostrophic current field. The uncertainty on the transport estimate, related to the error on the prediction of particle concentrations from light-transmission data and the error on velocities, is assessed. The particulate matter inflow entering the Gulf of Lion off Marseille is comparable to the Rhône River input and varies seasonally with a maximum transport between autumn and spring. These modifications result from variations of the water flux rather than variations of the particulate matter concentration. Residual transports of particulate matter and organic carbon across the entire Gulf of Lion are calculated for two cruises enclosing the domain that were performed in February 1995 and July 1996. The particulate matter budgets indicate a larger export from the shelf to deep ocean in February 1995 (110 ± 20 kg/s) than in July 1996 (25 ± 18 kg/s). Likewise, the mean particulate organic carbon export is 12.8 ± 0.5 kg/s in February 1995 and 0.8 ± 0.2 kg/s in July 1996. This winter increase is due to larger allochthonous and autochthonous inputs and also to enhanced shelf-slope exchange processes, in particular the cascading of cold water from the shelf. The export of particulate matter by the horizontal currents is moreover two orders of magnitude larger than the vertical particulate fluxes measured at the same time with sediment traps on the continental slope.

Study on the response of IFMIF fission chambers to mixed neutron-gamma fields: PH-2 experimental tests

The engineering design of fissionchambers as on-line radiation detectors for IFMIF is being performed in the framework of the IFMIF-EVEDA works. In this paper the results of the experiments performed in the BR2 reactor during the phase-2 of the foreseen validation activities are addressed. Two detectors have been tested in a mixedneutron-gamma field with high neutron fluence and gamma absorbed dose rates, comparable with the expected values in the HFTM in IFMIF. Since the neutron spectra in all BR2 channels are dominated by the thermal neutron component, the detectors have been surrounded by a cylindrical gadolinium screen to cut the thermal neutron component, in order to get a more representative test for IFMIF conditions. The integrated gamma absorbed dose was about 4 × 1010 Gy and the fast neutron fluence (E > 0.1 MeV) 4 × 1020 n/cm2. The fissionchambers were calibrated in three BR2 channels with different neutron-to-gamma ratio, and the long-term evolution of the signals was studied and compared with theoretical calculations

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Adaptive control system based on lineal control theory for the path-following problem of a car-like mobile robot

The objective of this paper is to design a path following control system for a car-like mobile robot using classical linear control techniques, so that it adapts on-line to varying conditions during the trajectory following task. The main advantages of the proposed control structure is that well known linear control theory can be applied in calculating the PID controllers to full control requirements, while at the same time it is exible to be applied in non-linear changing conditions of the path following task. For this purpose the Frenet frame kinematic model of the robot is linearised at a varying working point that is calculated as a function of the actual velocity, the path curvature and kinematic parameters of the robot, yielding a transfer function that varies during the trajectory. The proposed controller is formed by a combination of an adaptive PID and a feed-forward controller, which varies accordingly with the working conditions and compensates the non-linearity of the system. The good features and exibility of the proposed control structure have been demonstrated through realistic simulations that include both kinematics and dynamics of the car-like robot.

ANN and Fuzzy c-Means applied to environmental pollution prediction

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.