Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

Objective: To compare the efficacy of Intradermal Tranexamic acid and topical 20% Azelaic acid cream in the treatment of melasma. Study Design: Comparative prospective study. Place and Duration of Study: Dermatology department, Combined Military Hospital Peshawar, from Sep 2018 to Mar 2019. Methodology: A total of 116 female patients, at the outpatient department of dermatology at Combined Military Hospital Peshawar, were randomly assigned into two groups; group A (intradermal tranexamic acid) and group B (topical azelaic acid) by lottery method. Patients in group A received intradermal injection, while the participants of group B received topical azelaic acid only, fortnightly for 6 weeks. Melasma area severity index score was calculated for each patient in both groups at the start and at the end of the treatment. Results: The mean Melasma area severity index score in group A (intradermal tranexamic acid) before and at 6 weeks of treatment was 7.10 ± 2.94 and 5.27 ± 2.44, respectively. The mean Melasma area severity index score in group B (topical azelaic acid) before and at 6 weeks of treatment was 7.56 ± 2.57 and 5.76 ± 2.89, respectively. Efficacy of intradermal tranexamic acid, as poor response, good response and excellent response was 27.6%, 41.4% and 31% respectively. While, efficacy of topical azelaic acid group as poor response, good response and excellent response was 62.1%, 20.7% and 17.2% respectively. The difference was statistically significant, (p=0.001). Conclusion: It can be concluded that intradermal tranexamic acid is more effective as compared to topical 20% azelaic acid in..................

Objective: To compare the efficacy of topical 5% Nicotinamide gel versus 2% Clindamycin gel in patients with mild to moderate acne. Study Design: Comparative cross-sectional study. Place and Duration of Study: Department of Dermatology, Combined Military Hospital, Quetta Pakistan, from Jan to Jun 2019. Methodology: Patients with mild to moderate acne were enrolled in the study. A total of 372 patients were randomly and equally divided into two groups, Group-A (Clindamycin) and Group-B (Nicotinamide). Response to treatment was graded according to Acne Global Severity Score. Scoring was done in both groups at the start and after eight weeks of therapy.Therapy was considered efficacious if there was at least 2 step improvement in post-therapy scores compared to pre-therapy scores.Results: Total number of patients included was 372. Group-A (Clindamycin-Group) had 186 patients, of which 67 were males,and 119 were females. In Group-B (Nicotinamide-Group), out of 186 patients, 62 were males, and 124 were females. Regarding the treatment results, Clindamycin was found to be 31% efficacious, whereas the efficacy of Nicotinamide was 34.7% (pvalue=0.127). Conclusion: There is no significant difference in the efficacy of Clindamycin and Nicotinamide in treating mild to moderate acne.