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Nucleus pH is closely linked to many diseases such as aging, heart disease, skeletal myopathies, cancer, Alzheimer's disease, etc. Nevertheless, fluorescent sensors that can directly monitor nucleus pH changes have not yet been reported. Here, we first reported a green emissive carbon dots (CDs) for nucleus pH detection in living cells. CDs can selectively target nucleus with high accumulation at nucleolus due to their high affinity towards RNA once entering cells by lipid raft mediated endocytosis. Without washing, CDs at 5 μg/mL was enough to lighten nucleus within 10 min with the fluorescence on ever after 24 h incubation, achieving fast, wash-free, and long-term nucleus/nucleolus imaging. Meanwhile, the luminescent intensity of CDs was reduced gradually when pH changed continuously from 1 to 12, showing a pH-responsive fluorescence property with two linear ranges of pH 2-7 and pH 7-12. With their nucleus-targeting ability and pH-dependent photoluminescent property, CDs was successfully leveraged for nucleus pH detection in A549 cells and for in vivo pH sensing in zebra fish. CDs present a promising and powerful fluorescent sensor for nucleus imaging and nucleus pH sensing in living cells on the way to understand nucleus-related biological events., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Snow is a crucial component of the hydrological cycle in the Western Himalaya. Water from snowmelt is used in various sectors in downstream regions, thus playing a critical role in securing the livelihoods of millions of people. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment of Afghanistan, a sub-basin of the Indus, in the Western Himalaya. We applied a three-step approach to select a few global climate model (GCM) simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5, which showed reasonable performance with ERA5-Land dataset for the chosen historical period (1981-2010). The selected model simulations were then segregated into two groups: those projecting a cold-wet climate and those projecting a warm-dry climate by the end of the 21 st century (2071-2100). These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. The results indicate that the model captures well the snow cover dynamics for the historical period when compared with the daily MODIS-derived snow cover. The J2000 model was then forced by climate projections from the selected GCMs to quantify future changes in snow cover area, snow storage and snowmelt. While a 10-18% reduction in annual snow cover area is projected in the cold-wet models, a 22-36% reduction is projected in the warm-dry models. Similarly, the snow cover area is projected to decrease in all elevation bands under climate change. At the seasonal scale, across all models and scenarios, the snow cover in the autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources are likely to have direct implications for water-dependent sectors in the region and call for a better understanding of water usage and future adaptation practices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)