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During pure oxygen ventilation, logarithmically transformed PaO2/Fio2 allows estimation of CT shunt and its changes in patients during systemic inflammation. Relevant intrapulmonary shunting seems to occur in lung regions with CT numbers between [-200 and +100] Hounsfield Units.

When analyzing version histories, researchers traditionally focused on single events: e.g. the change that causes a bug, the fix that resolves an issue. Sometimes however, there are indirect effects that count: Changing a module may lead to plenty of follow-up modifications in other places, making the initial change having an impact on those later changes. To this end, we group changes into change genealogies, graphs of changes reflecting their mutual dependencies and influences and develop new metrics to capture the spatial and temporal influence of changes. In this paper, we show that change genealogies offer good classification models when identifying defective source files: With a median precision of 73% and a median recall of 76%, change genealogy defect prediction models not only show better classification accuracies as models based on code complexity, but can also outperform classification models based on code dependency network metrics.

IntroductionComputed tomography (CT) is considered the gold standard for quantification of global or regional lung aeration and lung mass. Quantitative CT, however, involves the exposure to ionizing radiation and requires manual image processing. We recently evaluated an extrapolation method which calculates quantitative CT parameters characterizing the entire lung from only 10 reference CT-slices thereby reducing radiation exposure and analysis time. We hypothesized that this extrapolation method could be further validated using CT-data from pigs and sheep, which have a different thoracic anatomy.MethodsWe quantified volume and mass of the total lung and differently aerated lung compartments in 168 ovine and 55 porcine whole-lung CTs covering lung conditions from normal to gross deaeration. Extrapolated volume and mass parameters were compared to the respective values obtained by whole-lung analysis. We also tested the accuracy of extrapolation for all possible numbers of CT slices between 15 and 5. Bias and limits of agreement (LOA) were analyzed by the Bland-Altman method.ResultsFor extrapolation from 10 reference slices, bias (LOA) for the total lung volume and mass of sheep were 18.4 (-57.2 to 94.0) ml and 4.2 (-21.8 to 30.2) grams, respectively. The corresponding bias (LOA) values for pigs were 5.1 (-55.2 to 65.3) ml and 1.6 (-32.9 to 36.2) grams, respectively. All bias values for differently aerated lung compartments were below 1% of the total lung volume or mass and the LOA never exceeded ± 2.5%. Bias values diverged from zero and the LOA became considerably wider when less than 10 reference slices were used.ConclusionsThe extrapolation method appears robust against variations in thoracic anatomy, which further supports its accuracy and potential usefulness for clinical and experimental application of quantitative CT.

Perceived travel time in public transport trip directly affects passengers' satisfaction and therefore is an essential consideration when planning and operating the public transport system. However, beyond the prevalent analysis on the waiting time perception, there are few articles that have concerned the travel time perception along the entire multimodal trip. In this context, this paper presents an empirical investigation of actual and perceived travel time at each stage in a bus-rail transport trip, where first mile, in-vehicle stage, transfer stage and last mile are all considered. Data on actual and perceived travel time, socioeconomic characteristics, trip characteristics and facility usage are collected by accompany survey undertaken from passengers' originations to destinations. The results from a series of paired T-tests show that passenger do perceive travel time to be greater than the actual amount at each stage. Three linear regression models are developed for estimation of perceived walking, waiting and in-vehicle time. Results indicate that socioeconomic characteristics, trip characteristics and facility usage seem to have an impact on passengers' travel time perception, while the travel time spent on the previous stage does not affect the perception too much.