Non-fibrillar soluble oligomeric forms of amyloid-β peptide (oAβ) and tau proteins are likely to play a major role in Alzheimer’s disease (AD). The prevailing hypothesis on the disease etiopathogenesis is that oAβ initiates tau pathology that slowly spreads throughout the medial temporal cortex and neocortices independently of Aβ, eventually leading to memory loss. Here we show that a brief exposure to extracellular recombinant human tau oligomers (oTau), but not monomers, produces an impairment of long-term potentiation (LTP) and memory, independent of the presence of high oAβ levels. The impairment is immediate as it raises as soon as 20 min after exposure to the oligomers. These effects are reproduced either by oTau extracted from AD human specimens, or naturally produced in mice overexpressing human tau. Finally, we found that oTau could also act in combination with oAβ to produce these effects, as sub-toxic doses of the two peptides combined lead to LTP and memory impairment. These findings provide a novel view of the effects of tau and Aβ on memory loss, offering new therapeutic opportunities in the therapy of AD and other neurodegenerative diseases associated with Aβ and tau pathology.

Abstract. Using 2B-CLDCLASS-LIDAR (radar–lidar) cloud classification and 2B-FLXHR-LIDAR radiation products from CloudSat over 4 years, this study evaluates the co-occurrence frequencies of different cloud types, analyzes their along-track horizontal scales and cloud radiative effects (CREs), and utilizes the vertical distributions of cloud types to evaluate cloud-overlap assumptions. The statistical results show that high clouds, altostratus (As), altocumulus (Ac) and cumulus (Cu) tend to coexist with other cloud types. However, stratus (St) (or stratocumulus, Sc), nimbostratus (Ns) and convective clouds are much more likely to exhibit individual features than other cloud types. On average, altostratus-over-stratus/stratocumulus cloud systems have a maximum horizontal scale of 17.4 km, with a standard deviation of 23.5 km. Altocumulus-over-cumulus cloud types have a minimum scale of 2.8 km, with a standard deviation of 3.1 km. By considering the weight of each multilayered cloud type, we find that the global mean instantaneous net CREs of multilayered cloud systems during the daytime are approximately −41.3 and −50.2 W m−2, which account for 40.1 and 42.3% of the global mean total net CREs at the top of the atmosphere (TOA) and at the surface, respectively. The radiative contributions of high-over-altocumulus and high-over-stratus/stratocumulus (or cumulus) in the all multilayered cloud systems are dominant due to their frequency. Considering the overlap of cloud types, the cloud fraction based on the random overlap assumption is underestimated over vast oceans, except in the west-central Pacific Ocean warm pool. Obvious overestimations mainly occur over tropical and subtropical land masses. In view of a lower degree of overlap than that predicted by the random overlap assumption to occur over the vast ocean, particularly poleward of 40° S, the study therefore suggests that a linear combination of minimum and random overlap assumptions may further improve the predictions of actual cloud fractions for multilayered cloud types (e.g., As + St/Sc and Ac + St/Sc) over the Southern Ocean. The establishment of a statistical relationship between multilayered cloud types and the environmental conditions (e.g., atmospheric vertical motion, convective stability and wind shear) would be useful for parameterization design of cloud overlap in numerical models.

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Abstract.A method is developed based on CloudAerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) level 1 attenuated backscatter profile data for deriving the mean extinction coefficient of water droplets close to cloud top. The method is applicable to low level (cloud top <2 km), opaque water clouds in which the lidar signal is completely attenuated beyond about 100 m of penetration into the cloud. The photo multiplier tubes (PMTs) of the 532 nm detectors (parallel and perpendicular polarizations) of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) both exhibit a non-ideal recovery of the lidar signal after striking a strongly backscattering target (such as water cloud or surface). Therefore, the effects of any transient responses of CALIOP on the attenuated backscatter profile of the water cloud must first be removed in order to obtain a reliable (validated) attenuated backscatter profile. Then, the slope of the exponential decay of the validated water cloud attenuated backscatter profile, and the multiple scattering factor are used for deriving the mean extinction coefficient of low-level water cloud droplets close to cloud top. This novel method was evaluated and compared with the previous method which combined the cloud effective radius (3.7-µm) reported by MODIS with the lidar depolarization ratios measured by CALIPSO to estimate the mean extinction coefficient. Statistical results show that the extinction coefficients derived by the new method based on CALIOP alone agree reasonbably well with those obtained in the previous study using combined CALIOP and MODIS data. The mean absolute relative difference in extinction coefficient is about 13.4%. An important advantage of the new method is that it can be used to derive the Correspondence to: Y. Hu (yongxiang.hu-1@nasa.gov) extinction coefficient also during night time, and it is also applicable when multi-layered clouds are present. Overall, the stratocumulus dominated regions experience larger day-night differences which are all negative and seasonal. However, a contrary tendency consisted in the global mean values. The global mean cloud water extinction coefficients during different seasons range from 26 to 30 km −1 , and the differences between day and night time are all positive and small (about 1-2 km −1 ). In addition, the global mean layer-integrated depolarization ratios of liquid water clouds during different seasons range from 0.2 to 0.23, and the differences between day and night also are small, about 0.01.

Topography and spatial patterns of landscape significantly affect spatial distribution of precipitation and, in turn, hydrological modelling, especially in high elevation, mountainous watersheds of arid regions. This study incorporates a physically based inverse distance and elevation weighted (PBIDEW) method into a distributed conceptual hydrological model, distributed large basin runoff model, and compared with an inverse distance weighted (IDW) method to assess the performances of both methods in precipitation estimation for hydrological modelling at watershed scale. The PBIDEW method considers the impacts of topography using month‐dependent parameters in its interpolation of meteorological variables while the IDW method does not. Both the IDW and the PBIDEW methods are evaluated and compared in hydrological modelling at different spatial resolutions in the upper reach of the Heihe River Watershed, Northwest China. Results show that the IDW method underestimated the areal precipitation, and the PBIDEW method produced more realistic precipitation estimations in the study area. Both methods have some limitations, the performance of the IDW method was mainly influenced by the availability of observation data, while that of the PBIDEW method was mainly influenced by the representation of topographical information. Considering more detailed information for precipitation estimates, the PBIDEW method performed better at finer spatial resolution. Overall, the PBIDEW method, using month‐dependent physical interpolation parameters, seems more suitable for precipitation estimation in hydrological simulations in data‐scarce, high elevation and topographically complex mountainous watersheds in arid area. Copyright © 2017 John Wiley & Sons, Ltd.

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