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Lysosomes are cell organelles that degrade macromolecules to recycle their components. If lysosomal degradative function is impaired, e.g., due to mutations in lysosomal enzymes or membrane proteins, lysosomal storage diseases (LSDs) can develop. LSDs manifest often with neurodegenerative symptoms, typically starting in early childhood, and going along with a strongly reduced life expectancy and quality of life. We show here that small molecule activation of the Ca 2+ ‐permeable endolysosomal two‐pore channel 2 (TPC2) results in an amelioration of cellular phenotypes associated with LSDs such as cholesterol or lipofuscin accumulation, or the formation of abnormal vacuoles seen by electron microscopy. Rescue effects by TPC2 activation, which promotes lysosomal exocytosis and autophagy, were assessed in mucolipidosis type IV (MLIV), Niemann–Pick type C1, and Batten disease patient fibroblasts, and in neurons derived from newly generated isogenic human iPSC models for MLIV and Batten disease. For in vivo proof of concept, we tested TPC2 activation in the MLIV mouse model. In sum, our data suggest that TPC2 is a promising target for the treatment of different types of LSDs, both in vitro and in‐vivo . Synopsis Mutations in proteins of the endolysosomal machinery such as lysosomal enzymes or lysosomal membrane proteins often result in severe neurodegenerative disease, including mucolipidosis type IV, Niemann Pick type C1, or Batten disease. Small molecule activation of the endolysosomal two‐pore channel 2 (TPC2) results in an amelioration of cellular phenotypes associated with lysosomal storage diseases (LSDs), such as the accumulation of cholesterol, lactosylceramide, or lipofuscin, or the formation of abnormal vacuoles. Rescue effects of the TPC2 agonist are demonstrated in LSD patient fibroblasts and neurons derived from newly generated isogenic human iPSC models for MLIV and Batten disease. Mechanistically, the fully preserved capability of TPC2 to promote lysosomal exocytosis and autophagy in these diseases is demonstrated, suggesting rescue effects by clearing intracellular debris. Expression patterns of TPC2 in the brain are examined using a newly engineered TPC2‐GFP reporter mouse model, complemented by qPCR analyses of human and mouse brain samples. In vivo efficacy of the PI(3,5)P2‐mimetic TPC2 agonist TPC2‐A1‐P is shown, restoring pathological hallmarks in a mouse model of MLIV. Graphical Abstract Mutations in proteins of the endolysosomal machinery such as lysosomal enzymes or lysosomal membrane proteins often result in severe neurodegenerative disease, including mucolipidosis type IV, Niemann–Pick type C1, or Batten disease.

Digital elevation model (DEM) data are essential to hydrological applications and have been widely used to calculate a variety of useful topographic characteristics, e.g., slope, flow direction, flow accumulation area, stream channel network, topographic index, and others. Except for slope, none of the other topographic characteristics can be calculated until the flow direction at each pixel within a DEM is determined. However, flow direction cannot be accurately calculated until depressions and flat areas within a DEM have been rectified. This is a routine problem in hydrologic modeling, because virtually all DEMs contain flat and sink pixels, both real and artifactual, that if left untreated will prevent accurate simulation of hydrologic flow paths. Although a number of algorithms are available for rectifying flat and sink pixels in DEM data, treatment of flat areas and depressions and calculation of flow direction remain problematic for reasons of complexity and uncertainty. A new algorithm that effectively rectifies flat and sink pixels was developed and tested. The approach is to use linear interpolation between low elevation grid cells on the edge of each flat area or depression defined as outlets and higher elevation grid cells on the opposite side defined as inflow pixels. The implementation requires an iterative solution to accommodate the irregular geometry of flat areas or depressions and exceptions that arise. Linear interpolation across flat areas or depressions provides a natural way to scale elevation adjustments based on the vertical scale of the surrounding topography, thereby avoiding the addition or subtraction of arbitrary small numbers that we regard as a disadvantage in some prior techniques. Tests for two virtual terrains and one real terrain show that our algorithm effectively rectifies flat areas and depressions, even in low‐relief terrain, and produces realistic patterns of flow accumulation and extracted channel networks. Key Points Treat flat areas and depressions (FADs) in DEMs using linear interpolation The proposed algorithm was integrated in a DEM processing tool called PDEM PDEM is better than ArcGIS and TOPAZ for extracting drainage networks from DEMs

Hyperpolarization-activated cyclic nucleotide-gated channels (HCNs) in the nervous system are implicated in a variety of neuronal functions including learning and memory, regulation of vigilance states and pain. Dysfunctions or genetic loss of these channels have been shown to cause human diseases such as epilepsy, depression, schizophrenia, and Parkinson's disease. The physiological functions of HCN1 and HCN2 channels in the nervous system have been analyzed using genetic knockout mouse models. By contrast, there are no such genetic studies for HCN3 channels so far. Here, we use a HCN3-deficient (HCN3 ) mouse line, which has been previously generated in our group to examine the expression and function of this channel in the CNS. Specifically, we investigate the role of HCN3 channels for the regulation of circadian rhythm and for the determination of behavior. Contrary to previous suggestions we find that HCN3 mice show normal visual, photic, and non-photic circadian function. In addition, HCN3 mice are impaired in processing contextual information, which is characterized by attenuated long-term extinction of contextual fear and increased fear to a neutral context upon repeated exposure.

This study explores the relationship between the normalized difference vegetation index (NDVI), aboveground plant biomass, and ecosystem C fluxes including gross ecosystem production (GEP), ecosystem respiration (ER) and net ecosystem production. We measured NDVI across long-term experimental treatments in wet sedge tundra at the Toolik Lake LTER site, in northern Alaska. Over 13 years, N and P were applied in factorial experiments (N, P and N + P), air temperature was increased using greenhouses with and without N + P fertilizer, and light intensity (photosynthetically active photon flux density) was reduced by 50% using shade cloth. Within each treatment plot, NDVI, aboveground biomass and whole-system CO₂ flux measurements were made at the same sampling points during the peak-growing season of 2001. We found that across all treatments, NDVI is correlated with aboveground biomass (r2=0.84), GEP (r2=0.75) and ER (r2=0.71), providing a basis for linking remotely sensed NDVI to aboveground biomass and ecosystem carbon flux.

The three-layer snow model of Lynch-Stieglitz is coupled to the global catchment-based land surface model of the National Aeronautics and Space Administration’s Seasonal to Interannual Prediction Project, and the combined models are used to simulate the growth and ablation of snow cover over the North American continent for the period of 1987–88. The various snow processes included in the three-layer model, such as snow melting and refreezing, dynamic changes in snow density, and snow insulating properties, are shown (through a comparison with the corresponding simulation using a much simpler snow model) to lead to an improved simulation of ground thermodynamics on the continental scale. This comparison indicates that the three-layer model, originally developed and validated at small experimental catchments, does indeed capture the important snow processes that control the growth and the ablation of continental-scale snowpack and its snow insulation capabilities.

The current generation of land-surface models used in GCMs view the soil column as the fundamental hydrologic unit. While this may be effective in simulating such processes as the evolution of ground temperatures and the growth/ablation of a snowpack at the soil plot scale, it effectively ignores the role topography plays in the development of soil moisture heterogeneity and the subsequent impacts of this soil moisture heterogeneity on watershed evapotranspiration and the partitioning of surface fluxes. This view also ignores the role topography plays in the timing of discharge and the partitioning of discharge into surface runoff and baseflow. In this paper an approach to land-surface modeling is presented that allows us to view the watershed as the fundamental hydrologic unit. The analytic form of TOPMODEL equations are incorporated into the soil column framework and the resulting model is used to predict the saturated fraction of the watershed and baseflow in a consistent fashion. Soil moisture heterogeneity represented by saturated lowlands subsequently impacts the partitioning of surface fluxes, including evapotranspiration and runoff. The approach is computationally efficient, allows for a greatly improved simulation of the hydrologic cycle, and is easily coupled into the existing framework of the current generation of single column land-surface models. Because this approach uses the statistics of the topography rather than the details of the topography, it is compatible with the large spatial scales of today's regional and global climate models. Five years of meteorological and hydrological data from the Sleepers River watershed located in the northeastern United States where winter snow cover is significant were used to drive the new model. Site validation data were sufficient to evaluate model performance with regard to various aspects of the watershed water balance, including snowpack growth/ablation, the spring snowmelt hydrograph, and storm hydrographs

Forest harvest effects on streamflow generation have been well described experimentally, but a clear understanding of process‐level hydrological controls can be difficult to ascertain from data alone. We apply a new model, Visualizing Ecosystems for Land Management Assessments (VELMA), to elucidate how hillslope and catchment‐scale processes control stream discharge in a small Pacific Northwest catchment. VELMA is a spatially distributed ecohydrology model that links hydrological and biogeochemical processes within watersheds. The study site is WS10 of the H.J. Andrews LTER, a 10 ha forested catchment clearcut in 1975. Simulated and observed daily streamflow are in good agreement for both the pre‐ (1969–1974) and postharvest (1975–2008) periods (Nash‐Sutcliffe efficiency = 0.807 and 0.819, respectively). One hundred scenarios, where harvest amounts ranged from 2% to 100% were conducted. Results show that (1) for the case of a 100% clearcut, stream discharge initially increased by ∼29% or 345 mm but returned to preclearcut levels within 50 years, and (2) annual streamflow increased at a near linear rate of 3.5 mm year−1 for each percent of catchment harvested, irrespective of location. Thereafter, to assess the impact of harvest location on stream discharge, 20 harvest scenarios were simulated, where harvest amount was fixed at 20% but harvest location varied. Results show that the streamflow response is strongly sensitive to harvest distance from the stream channel. Specifically, a 20% clearcut area near the catchment divide resulted in an average annual streamflow increase of 53 mm, whereas a 20% clearcut near the stream resulted in an average annual streamflow increase of 92 mm. Key Points Linear increase in stream discharge with increasing harvest area Large absolute fall changes and large relative summer changes in streamflow Changes in streamflow were strongly sensitive to harvest location

Five years of meteorological and hydrological data from a typical New England watershed where winter snow cover is significant were used to drive and validate two off-line land surface schemes suitable for use in the Goddard Institute for Space Studies GCM: a baseline scheme that does not model the physics of a snowpack and therefore, neglects the insulating properties of snow cover; and a modified scheme in which a three-layer snowpack is modeled. Comparing baseline model results with validation data reveals several model deficiencies. Surface radiation temperatures could not adequately be modeled and the ground froze to unreasonable depths. Furthermore, because of ground cooling resulting from large surface heat fluxes to the atmosphere from the uninsulated surface, deeper model layers did not unfreeze until midsummer. As such, the normal hydrologic processes of runoff, ground water infiltration, and movement, etc., are compromised for a good part of the year. With the inclusion of a simple three-layer snow model into the baseline model, not only are the ground and surface radiation temperatures adequately modeled but all the features of snowpack ripening that characterize pack growth/ablation are simulated.

Context The > 25,000 km 2 Flint Hills ecoregion in eastern Kansas and northeastern Oklahoma, USA, is an economically and ecologically important area encompassing the largest remaining tallgrass prairie ecosystem in North America. Prescribed fires are used routinely to control invasive woody species and improve forage production for the beef-cattle industry. However, burning releases harmful pollutants that, at times, contribute to air quality problems for communities across a multi-state area. Objectives Establish a modeling framework for synthesizing long-term ecological data in support of Flint Hills tallgrass prairie management goals for identifying how much, where, and when rangeland burning can be conducted to maximize ecological and economic benefits while minimizing regional air quality impacts. Methods We used EPA’s VELMA ecohydrology model to synthesize long-term experimental data at the 35 km 2 Konza Prairie Biological Station (KPBS) describing the effects of climate, fire, grazing, topography, and soil moisture and nutrient dynamics on tallgrass prairie productivity and fuel loads; and to spatially extrapolate that synthesis to estimate grassland productivity and fuel loads across the nearly 1000 times larger Flint Hills ecoregion to support prescribed burning smoke trajectory modeling using the State of Kansas implementation of the U.S. Forest Service BlueSky framework. Results VELMA provided a performance-tested synthesis of KPBS data from field observations and experiments, thereby establishing a tool for regionally simulating the combined effects of climate, fire, grazing, topography, soil moisture, and nutrients on tallgrass prairie productivity and fuel loads. VELMA’s extrapolation of that synthesis allowed difficult-to-quantify fuel loads to be mapped across the Flint Hills to support environmental decision making, such as forecasting when, where, and how prescribed burning will have the least impact on downwind population centers. Conclusions Our regional spatial and temporal extrapolation of VELMA’s KPBS data synthesis posits that the effects of integrated ecohydrological processes operate similarly across tallgrass prairie spatial scales. Based on multi-scale performance tests of the VELMA-BlueSky toolset, our multi-institution team is confident that it can assist stakeholders and decision makers in realistically exploring tallgrass prairie management options for balancing air quality, tallgrass prairie sustainability, and associated economic benefits for the Flint Hills ecoregion and downwind communities.

Wetland ecosystems are often characterized by distinct vegetation patterns. Various mechanisms have been proposed to explain the formation of these patterns; including spatially variable peat accumulation and water ponding. Recently, short‐range facilitation and long‐range competition for resources (a.k.a scale dependent feedback) has been proposed as a possible mechanism for pattern formation in wetland ecosystems. We modify an existing, spatially explicit, advection‐reaction‐diffusion model to include for a regional hydraulic gradient and effective anisotropy in hydraulic conductivity. This effective anisotropic hydraulic conductivity implicitly represents the effect of ponding: a reduction in the long‐range inhibition of vegetation growth in the direction perpendicular to the prevailing hydraulic gradient. We demonstrate that by accounting for effective anisotropy in a simple modeling framework that encompasses only a scale dependent feedback between biomass and nutrient flow, we can reproduce the various vegetation patterns observed in wetland ecosystems: maze, and vegetation bands both perpendicular and parallel to prevailing flow directions. We examine the behavior of this model over a range of plant transpiration rates and regional hydraulic gradients. Results show that by accounting for the effective x‐y anisotropy that results from biomass‐water interaction (i.e., ponding) we can better understand the mechanisms that drive ecosystem patterning.