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The dif®culties in using complicated models of carbon mineralization and the poor performance of simple ones call for new models that are simple in use and robust in performance. We have developed a model for the mineralization of carbon from experimental data in which the organic matter is treated as a single component. The logarithm of the average relative mineralization rate, K, or rate constant, of a substrate considered as a whole was found to be linearly related to the logarithm of time, t, provided prevailing soil conditions remained unchanged. The equation is: logK = logR ± S logt, or K = R t ±S , in which R (dimension t S ± 1 ) represents K at t = 1, and S (dimensionless, 1 > S > 0) is a measure of the rate at which K decreases over time, also called the speed of`ageing' of the substrate. The quantity of the remaining substrate, Y t , is calculated by Y t = Y 0 exp(±Rt 1 ± S ), where Y 0 is the initial quantity of the substrate. The actual relative mineralization rate, k, at time t is proportional to K, according to k = (1 ± S)K. The model was tested against an assembly of 136 sets of data collected from trials conducted in 14 countries all over the world. They cover materials ranging from glucose, cellulose and plant residues, to farmyard manure, peat and soil organic matter. The results lead to the conclusion that the model describes well the dynamics of organic matter in soil over time varying from months to tens of years, provided major environmental conditions remain unchanged. It can easily be applied in practice and is attractive because of its modest input requirements.Correspondence: B. H. Janssen.

PurposeIncidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection.MethodsGender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30–80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics.ResultsAge, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women.ConclusionsColorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

PurposeLung cancer is the leading cause of cancer deaths in Korea. The objective of the present study was to develop an individualized risk prediction model for lung cancer in Korean men using population-based cohort data.MethodsFrom a population-based cohort study of 1,324,804 Korean men free of cancer at baseline, the individualized absolute risk of developing lung cancer was estimated using the Cox proportional hazards model. We checked the validity of the model using C statistics and the Hosmer–Lemeshow chi-square test on an external validation dataset.ResultsThe risk prediction model for lung cancer in Korean men included smoking exposure, age at smoking initiation, body mass index, physical activity, and fasting glucose levels. The model showed excellent performance (C statistic = 0.871, 95% CI = 0.867–0.876). Smoking was significantly associated with the risk of lung cancer in Korean men, with a four-fold increased risk in current smokers consuming more than one pack a day relative to non-smokers. Age at smoking initiation was also a significant predictor for developing lung cancer; a younger age at initiation was associated with a higher risk of developing lung cancer.ConclusionThis is the first study to provide an individualized risk prediction model for lung cancer in an Asian population with very good model performance. In addition to current smoking status, earlier exposure to smoking was a very important factor for developing lung cancer. Since most of the risk factors are modifiable, this model can be used to identify those who are at a higher risk and who can subsequently modify their lifestyle choices to lower their risk of lung cancer.