Hardware development for the Bone Proteomics experiment

This paper gives a quick overview of how various scientific, operational and safety related requirements drove the development of hardware for the Bone Proteomics (BOP) experiment that was conducted on the International Space Station during the Italian Soyuz Mission in 2005. The experiment objectives will be highlighted briefly and design solutions will be presented. Comments will be given regarding the choice of a particular design solution and the impact on the complexity, cost and development time. Conclusions in this paper are based on the experience gained when developing the BOP experiment hardware and other relatively small experiment hardware packages, typical of European Soyuz Missions. The observations should not be extrapolated to large payloads. The objective of this paper is not to produce a recipe for developing experiment hardware; dedicated documents for that purpose are available elsewhere. Rather, the objective is to help others profit from experience gained in the development of relatively small experiment hardware packages, and to highlight where time and cost saving decisions can be made.     

On the effectiveness of early life cycle defect prediction with Bayesian Nets

Standard practice in building models in software engineering normally involves three steps: collecting domain knowledge (previous results, expert knowledge); building a skeleton of the model based on step 1 including as yet unknown parameters; estimating the model parameters using historical data. Our experience shows that it is extremely difficult to obtain reliable data of the required granularity, or of the required volume with which we could later generalize our conclusions. Therefore, in searching for a method for building a model we cannot consider methods requiring large volumes of data. This paper discusses an experiment to develop a causal model (Bayesian net) for predicting the number of residual defects that are likely to be found during independent testing or operational usage. The approach supports (1) and (2), does not require (3), yet still makes accurate defect predictions (an R ² of 0.93 between predicted and actual defects). Since our method does not require detailed domain knowledge it can be applied very early in the process life cycle. The model incorporates a set of quantitative and qualitative factors describing a project and its development process, which are inputs to the model. The model variables, as well as the relationships between them, were identified as part of a major collaborative project. A dataset, elicited from 31 completed software projects in the consumer electronics industry, was gathered using a questionnaire distributed to managers of recent projects. We used this dataset to validate the model by analyzing several popular evaluation measures (R ², measures based on the relative error and Pred). The validation results also confirm the need for using the qualitative factors in the model. The dataset may be of interest to other researchers evaluating models with similar aims. Based on some typical scenarios we demonstrate how the model can be used for better decision support in operational environments. We also performed sensitivity analysis in which we identified the most influential variables on the number of residual defects. This showed that the project size, scale of distributed communication and the project complexity cause the most of variation in number of defects in our model. We make both the dataset and causal model available for research use.     

Estimating thermal stress in BIPV modules

The thermal stress on building‐integrated photovoltaic modules (BIPV) in Espoo, Finland, was studied with field‐testing of amorphous silicon modules. Based on these results, the thermal stress at two other European locations (Paris and Lisbon) was estimated. The estimation procedure entailed thermal modelling of heat transfer in the façade with meteorological data as input. The results indicate that the thermal stress on BIPV modules in Lisbon is, in this case, approximately 50% higher that in Espoo and between 80 and 200% higher than in Paris, depending on the activation energy of the degradation process. The difference in stress between a BIPV module and a free‐standing module in Espoo was 50–200%. Copyright © 2006 John Wiley & Sons, Ltd.     

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Expert identification and selection: Legal liability concerns and directions

Legal liabilities pertaining to the identification and selection of domain experts is an issue that could adversely impact expert systems developers. Problems pertaining to flawed knowledge, improperly defined expertise, and behavioural and psychological impediments are just some of the issues. This paper examines the torts of strict products liability and negligence that system developers could incur as a result of expert-related difficulties. Parallels from legal scholars and federal and state court decisions are discussed relevant to expert system projects and developers. The paper concludes with a presentation of steps that systems developers can take to minimize potential legal liability.     

Design and Characterization of the CLIMPAQ,Chamber for Laboratory Investigations of Materials,Pollution and Air Quality*

A test chamber has been developed in order to provide a small and simple emission testing facility capable of testing construction products in a climate where the important climatic parameters such as temperature, ventilation rate and air velocity can be varied independently around typical indoor values. The test chamber CLIMPAQ is made of panes of window glass. Other main surface materials are stainless steel and eloxated aluminium. The chamber has a volume of 50.9 litres and is designed to meet the requirements for quantifying air pollution. In this investigation human subjects acted as air pollution judges, and chemical characterization of the air pollution was carried out. Carpet, linoleum, wall paint and seal- ant were tested simultaneously in the CLIMPAQ and in four other chambers ranging from a full-scale chamber of 28 m³ to a field and laboratory emission cell of 3.5· 10^?5m³. Product ranking is the same in all chambers for the sensory measurements. Emission rates based on sensory measurements differ for all products less than 100 % except for tests in a 3-litre chamber where emission rates were higher. Chemical measurements differ up to approximately 10 times for the same product in different chambers. Deviations appear to be the result of different environmental parameters in the various chambers. Low air concentrations or high specific ventilation rates seem to increase emissions, while differences in air velocities and sink properties may also be the cause of differences in emission rates.     

Methotrexate and 6-mercaptopurine maintenance therapy for childhood acute lymphoblastic leukemia: dose adjustments by white cell counts or by pharmacokinetic parameters?

In a consecutive study of 14 boys and 17 girls with non-B-cell ALL who were > or = 1 year of age at diagnosis, the degree of myelosuppression during the last year of MTX/6MP maintenance therapy was analyzed in relation to the erythrocyte concentration of MTX polyglutamates and 6-thioguanine nucleotides (E-MTX and E-6TGN, the respective major cytotoxic metabolites of MTX and 6MP). For each patient, E-MTX and E-6TGN levels were measured 2-15 (median, 6) and 2-17 (median, 7) times, respectively. From these measurements, arithmetic means of E-MTX and E-6TGN were calculated (mE-MTX and mE-6TGN, respectively). Since MTX and 6MP probably work synergistically, the product of mE-MTX and mE-6TGN was calculated for each patient (mE-MTX x 6TGN). The degree of myelosuppression was registered as the mean WBC determined following cessation of the therapy minus the mean WBC measured during the therapy (mWBCshift). The mean WBCs measured on therapy (mWBC(on)) and off therapy were highly correlated (r = 0.48, P = 0.009). The median mWBCshift was 2.7 x 10(9)/l (range, 1.4-4.8 x 10(9)/l). In a multivariate regression analysis, the best-fit model to predict the mWBCshift included mE-MTX x 6TGN, age at drug withdrawal, and mWBC in the order given [mWBCshift = 4.3 + 0.00089 x (mE-MTX x 6TGN) - 0.097 x age - 0.41 x mWBC(on); global rs = 0.66, P = 0.0002]. Thus, the patients with higher mE-MTX x 6TGN values, the younger patients, and the patients with the lowest WBC during therapy had the most pronounced degree of myelosuppression as measured by mWBCshift. These results indicate that E-MTX and E-6TGN may give a better reflection of the treatment intensity than do the WBCs alone.     

Augmented platelet aggregation as predictor of reocclusion after thrombolysis in acute myocardial infarction

RATIONALE: Reocclusion after thrombolysis diminishes the benefits of early reperfusion after acute myocardial infarction (AMI). No clinical or laboratory variables have been identified as predictors for reocclusion yet. METHODS AND RESULTS: To evaluate hemostatic variables as potential risk determinants platelet aggregation (PA, representing platelet activity), thrombin/antithrombin complexes (TAT, representing thrombin generation), and plasminogen activator inhibitor type 1 (PAI-1, representing endogenous fibrinolysis) were determined in 31 patients with AMI at 0, 1, 2. and 12 h after the start of thrombolysis as well as at hospital discharge. Reocclusion (defined as reinfarction or angiographically confirmed, clinically silent coronary reocclusion) occurred in 5 patients within 5-14 days and in 8 patients within 1 year. TAT plasma concentrations were lower in patients with reocclusion than in those without (9.9+/-5.7 vs. 22.9+/-22.2 ng/ml at 2 h, 6.5+/-3.1 vs. 1 1.2+/-6.4 ng/ml at 12 h, means+/-SD, p <0.05 each). Neither concentration nor activity of PAI-1 in plasma differed between both patient groups. However, both slope and maximum of PA (induced by 2 micromol/l ADP) were augmented in patients with reocclusion (slope: 39.4+/-1.7 vs. 32.5+/-7.4 at 2 h, p <0.001; 42.6+/-2.6 vs. 36.6+/-8.9 at 12 h, p <0.01). Results were independent of the thrombolytic agent used (alteplase or reteplase). A PA slope at 2 h higher than the average slope before thrombolysis (37.2+/-5.7) could be identified as best predictor for early (within 5-14 d, p=0.017, sensitivity 1.00, specificity 0.69) and late reocclusion (within 1 y, p=0.009, 0.88 and 0.74, respectively). CONCLUSIONS: Increased PA following coronary thrombolysis appears to be associated with early and late reocclusion. This marker could be useful in identifying patients who may benefit from more aggressive antiplatelet (such as GP IIb/IIIa receptor antagonists), interventional, or both strategies.