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AbstrActbackground. Previous studies found an increased risk of cancer in hospitalized asthma patients, but it is not known whether patients from primary health care show a similar risk pattern. In addition, it is unclear whether the diagnosis of asthma can influence the prognosis of subsequent cancer. Methods. Asthma patients were identified from Swedish inpatient, outpatient, and primary health care registers, and were linked to the Swedish Cancer Registry to identify subsequent diagnoses of cancer. Standardized incidence ratios (SIRs) were used to examine the risk of cancer in asthma patients compared with subjects without asthma. In addition, we used Cox proportional hazards regression to estimate hazard ratios (HRs) for mortality in patients with both asthma and cancer. results. A total of 10 649 cancers were diagnosed in patients with previous asthma, with a SIR of 1.19 (95% CI 1.17-1.21). A total of 15 cancer sites showed an increased incidence, whereas two cancer sites showed a decreased risk. Non-allergic asthma showed the highest risk of cancer (SIR  1.25, 95% CI 1.18-1.32), followed by unspecified asthma (SIR  1.22, 95% CI 1.19-1.25), status asthmaticus (SIR  1.19, 95% CI 1.02-1.39), and allergic asthma (SIR  1.14, 95% CI 1.06-1.22). The risk of cancer was similarly increased in asthma patients diagnosed in primary health care and those diagnosed in hospitals. Cancer patients with previous asthma had increased mortality, with a HR of 1.55 (95% CI 1.50-1.60). HRs ranged from 1.09 to 1.94 for different sites/types of cancer. conclusions. Patients with asthma, irrespective of whether they were treated in primary health care or hospitals, had an increased risk of cancer. In addition, cancer patients with previous asthma had a worse prognosis compared with those without asthma, suggesting that these patients may require a multidisciplinary approach to manage the comorbidity.

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In this article, we consider the partially observed optimal control problem for forward-backward stochastic systems with Markovian regime switching. A stochastic maximum principle for optimal control is developed using a variational method and filtering technique. Our theoretical results are applied to the motivating example of the risk minimization for portfolio selection.

Remote sensing end-products related to vegetation have potential applications in monitoring the health of crops. The sensitivity of a spectral index to crop stress determines its application prospect. Our aim in this study was to explore the sensitivity of a ratio vegetation index (RVI) to identify the damage caused by brown planthoppers (BPHs) on rice plants, and to evaluate the potential application of hyperspectral end-products to monitor population size of BPH. Different numbers of the second-instar nymphs were released onto potted rice at the tillering stage. The plants were exposed to BPH for two, four, six, and eight days, and reflectance from the damaged rice was measured using a hyperspectral spectroradiometer. Measurements were done again two, four, and six days after exposure (recover days), and then the spectral index RVI746/670 was compared among rice plants infested with different numbers of BPH. The relationships between RVI746/670, the number of BPH and exposure day were simulated by linear and curve models. BPH damage resulted in a decreased spectral index RVI746/670 of rice plants. RVI746/670 well indicated the damage of rice plants caused by six–eight BPH nymphs per plant in six–eight days, but the index could not identify the damage of these nymphs in two days. The RVI746/670 showed a two–four-day delay to indicate a slight BPH damage. The spectral index RVI746/670 could indicate the physiologic compensation of plants for the feeding of BPH and the post-effect of BPH damage on rice. The RVI746/670 of rice showed a quadratic curve relation with the number of BPH nymphs and a quadratic or linear relation with the exposure day. The recover day had no significant effects on RVI746/670. The RVI746/670 (Y) could be simulated by a quadratic surface model based on the number of BPH (N) and exposure day (T): Y = 3.09427 + 0.59111T + 0.44296N − 0.03683T2 − 0.03035N2 − 0.08188NT (R2 = 0.5228, p < 0.01). In summary, the spectral index RVI746/670 of rice is sensitive to damage caused by BPH.