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Background: In the photodynamic therapy (PDT) of non‐aggressive basal cell carcinomas (BCCs), 5‐aminolevulinic acid nanoemulsion (BF‐200ALA) has shown non‐inferior efficacy when compared with methyl aminolevulinate (MAL), a widely used photosensitizer. Hexyl aminolevulinate (HAL) is an interesting alternative photosensitizer. To our knowledge, this is the first study using HAL‐PDT in the treatment of BCCs. Objectives: To compare the histological clearance, tolerability (pain and post‐treatment reaction) and cosmetic outcome of MAL, BF‐200 ALA and low‐concentration HAL in the PDT of non‐aggressive BCCs. Methods: Ninety‐eight histologically verified non‐aggressive BCCs met the inclusion criteria, and 54 patients with 95 lesions completed the study. The lesions were randomized to receive LED‐PDT in two repeated treatments with MAL, BF‐200 ALA or HAL. Efficacy was assessed both clinically and confirmed histologically at three months by blinded observers. Furthermore, cosmetic outcome, pain, post‐treatment reactions fluorescence and photobleaching were evaluated. Results: According to intention‐to‐treat analyses, the histologically confirmed lesion clearance was 93.8% (95% confidence interval [CI] = 79.9–98.3) for MAL, 90.9% (95% CI = 76.4–96.9) for BF‐200 ALA and 87.9% (95% CI = 72.7–95.2) for HAL, with no differences between the arms (P = 0.84). There were no differences between the arms as regards pain, post‐treatment reactions or cosmetic outcome. Conclusions: Photodynamic therapy with low‐concentration HAL and BF‐200 ALA has a similar efficacy, tolerability and cosmetic outcome compared to MAL. HAL is an interesting new option in dermatological PDT, since good efficacy is achieved with a low concentration. [ABSTRACT FROM AUTHOR]

Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate. [ABSTRACT FROM AUTHOR]