Heart failure (HF) is the end-stage of cardiovascular diseases, which is associated with a high mortality rate and high readmission rate. Household early diagnosis and real-time prognosis of HF at bedside are of significant importance. Here, we developed a highly sensitive and quantitative household prognosis platform (termed as UC-LFS platform), integrating a smartphone-based reader with multiplexed upconversion fluorescent lateral flow strip (LFS). Dual-color core-shell upconversion nanoparticles (UCNPs) were synthesized as probes for simultaneously quantifying two target antigens associated with HF, i.e., brain natriuretic peptide (BNP) and suppression of tumorigenicity 2 (ST2). With the fluorescent LFS, we achieved the specific detection of BNP and ST2 antigens in spiked samples with detection limits of 5 pg/mL and 1 ng/mL, respectively, both of which are of one order lower than their clinical cutoff. Subsequently, a smartphone-based portable reader and an analysis app were developed, which could rapidly quantify the result and share prognosis results with doctors. To confirm the usage of UC-LFS platform for clinical samples, we detected 38 clinical serum samples using the platform and successfully detected the minimal concentration of 29.92 ng/mL for ST2 and 17.46 pg/mL for BNP in these clinical samples. Comparing the detection results from FDA approved clinical methods, we obtained a good linear correlation, indicating the practical reliability and stability of our developed UC-LFS platform. Therefore, the developed UC-LFS platform is demonstrated to be highly sensitive and specific for sample-to-answer prognosis of HF, which holds great potential for risk assessment and health monitoring of post-treatment patients at home.

[1] Snow albedo feedback (SAF) is important for global climate change, with strong regional impacts over northern continental areas. SAF calculated from the seasonal cycle is a good predictor of SAF in climate change among a suite of coupled climate models. A previous linear decomposition of the simulated total SAF (NET) found 80% was related to the albedo contrast of snow covered and snow-free land (SNC), and 20% was related to the temperature dependence of snow albedo (TEM). By contrast, recent work using snow cover and surface albedo derived from APP-x satellite observations found that TEM and SNC contributed almost equally to NET. In the present study, revised estimates of TEM and SNC for northern land areas are calculated for the period 1982-99 using a simplified and reproducible method for comparing SAF in models and observations. The observed NET is À1.11% K À1 , of which 69% comes from SNC and 31% from TEM; the approximate additivity of SNC and TEM indicates that these two terms fully explain the total SAF. Regionally, the SNC term dominates equatorward of 65 N, while TEM dominates over the Arctic. The mean of 17 CMIP3 climate models shows NET is 7% larger than observed, caused primarily by a bias in TEM equatorward of 65 N. A newer model (NCAR CCSM4) with improved land surface and snow schemes reproduces observed values of NET and SNC closely. However, TEM in all models examined is 50-100% weaker than observed over the Arctic. There is a strong correlation between SAF in the seasonal cycle and SAF in climate change for all components, but the correlation is weakest for TEM. The TEM term also exhibits a much larger spread in the seasonal cycle than in climate change, which partially explains a discrepancy between previous published studies examining TEM.Citation: Fletcher, C. G., H. Zhao, P. J. Kushner, and R. Fernandes (2012), Using models and satellite observations to evaluate the strength of snow albedo feedback,

[1] Observation based estimates of controls on snow albedo feedback (SAF) are needed to constrain the snow and albedo parameterizations in general circulation model (GCM) projections of air temperature over the Northern Hemisphere (NH) landmass. The total April-May NH SAF, corresponding to the sum of the effect of temperature on surface albedo over snow covered surfaces ('metamorphism') and over surfaces transitioning from snow covered to snow free conditions ('snow cover'), is derived with daily NH snow cover and surface albedo products using Advanced Very High Resolution Radiometer Polar Pathfinder satellite data and surface air temperature from ERA40 reanalysis data between 1982 -1999. Without using snow cover information, the estimated total SAF, for land surfaces north of 30°N, of À0.93 ± 0.06%K À1 was not significantly different (95% confidence) from estimates based on International Satellite Cloud Climatology Project surface albedo data. The SAF, constrained to only snow covered areas, grew to À1.06 ± 0.08%K À1 with similar magnitudes for the 'snow cover' and 'metamorphosis' components. The SAF pattern was significantly correlated with the 'snow cover' component pattern over both North America and Eurasia but only over Eurasia for the 'metamorphosis' component. However, in contrast to GCM model based diagnoses of SAF, the control on the 'snow cover' component related to the albedo contrast of snow covered and snow free surfaces was not strongly correlated to the total SAF. Citation: Fernandes, R

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[1] More than a century ago, Blanford suggested that an inverse relationship existed between summer rainfall over Northwest India and snow cover in the western Himalaya. Recently it has been found that there is a positive correlation between Tibetan snow cover and Indian summer monsoon rainfall (IMR), a result opposite to that of Blanford. In this paper, we attempt to reconcile these contradictory observations through the analysis of spatial and temporal variability of Tibetan snow cover and its relationship with the Indian summer monsoon. We show that there exists an east-west dipole-like correlation pattern between snow cover over the Tibetan plateau and IMR that underwent a change in sign around 1985. We argue that variability in the Tibetan plateau monsoon is responsible for the spatial and temporal variability in the relationship between Tibetan snow cover and the Indian summer monsoon.