This powerful new land surface modeling system integrates data from advanced observing systems to support improved forecast model initialization and hydrometeorological investigations. Land surface temperature and wetness conditions affect and are affected by numerous climatological, meteorological, ecological, and geophysical phenomena. Therefore, accurate, high-resolution estimates of terrestrial water and energy storages are valuable for predicting climate change, weather, biological and agricultural productivity, and flooding, and for performing a wide array of studies in the broader biogeosciences. In particular, terrestrial stores of energy and water modulate fluxes between the land and atmosphere and exhibit persistence on diurnal, seasonal, and interannual time scales. Furthermore, because soil moisture, temperature, and snow are integrated states, biases in land surface forcing data and parameterizations accumulate as errors in the representations of these states in operational numerical weather forecast and climate models and their associated coupled data assimilation systems. That leads to incorrect surface water and energy partitioning, and, hence, inaccurate predictions. Reinitialization of land surface states would mollify this problem if the land surface fields were reliable and available globally, at high spatial resolution, and in near-real time.A Global Land Data Assimilation System (GLDAS) has been developed jointly by scientists at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) in order to produce such fields. GLDAS makes use of the new generation of groundand space-based observation systems, which provide data to constrain the modeled land surface states. Constraints are applied in two ways. First, by forcing the land surface models (LSMs) with observationbased meteorological fields, biases in atmospheric model-based forcing can be avoided. Second, by employing data assimilation techniques, observations of land surface states can be used to curb unrealistic model states. Through innovation and an ever-improving conceptualization of the physics underlying earth system processes, LSMs have continued to evolve and to display an improved ability to simulate complex phenomena. Concurrently, increases in computing power and affordability are allowing global simulations to be run more routinely and with less processing time, at spatial resolutions that could only be simulated using supercomputers five years ago. GLDAS harnesses this low-cost computing power to integrate observationbased data products from multiple sources within a sophisticated, global, high-resolution land surface modeling framework.What makes GLDAS unique is the union of all of these qualities: it is a global, high-resolution, offline (uncoupled to the atmosphere) terrestrial modeling system that incorporates satellite-and ground-based observations in order to produce opt...

a b s t r a c tBased on the official data modeling, this paper studies the transmission process of the Corona Virus Disease 2019 . The error between the model and the official data curve is quite small. At the same time, it realized forward prediction and backward inference of the epidemic situation, and the relevant analysis help relevant countries to make decisions.

Additional supporting data may be found in the supplementary information of this article.Data S1. Methods. Table S1. Population study and RCM features of melanoma subtypes. Table S2. Absolute and relative frequencies of melanoma subtypes and biomarker staining. HIF-1a (a) and CD271 (b) expression was evaluated in the three types of malanomas, as selected by RCM. Abstract: Dandruff is a scalp disorder characterized by the formation of flaky white-yellowish scales due to an altered proliferation and differentiation status; a disrupted barrier function; a decrease in the level of hydration and of natural moisturizing factors (NMF) in the scalp, with a persistent and relapsing inflammatory condition. It was recently reported that an imbalance between bacterial and fungal species colonizing the scalp of French volunteers was associated with dandruff condition. The purpose of the present study was to analyze the major bacterial and fungal species present on the scalp surface of Chinese volunteers and to investigate possible region-related variation in the microbiota linked to dandruff condition. The data obtained from the Chinese populations were highly similar to those obtained in France, confirming that dandruff scalps are associated with a higher incidence of Malassezia restricta and Staphylococcal sp. The ratios of Malassezia to Propionibacterium and Propionibacterium to Staphylococcus were also significantly higher in the dandruff volunteers as compared to normal volunteers, suggesting that equilibrium between the major bacterial and fungal taxa found on the normal scalps is perturbed in the dandruff scalps. The main difference between the French and Shanghai subjects was in their Staphylococcal biota. The results obtained in China and in France suggest that targeting one particular Malassezia sp. by antifungals instead of using large spectrum antifungals and rebalancing the dandruff scalp microbiota could be common approach to improve dandruff condition in the two countries.