Long range experimental hydrologic forecasting for the eastern U.S.

We explore a strategy for long-range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Monthly ensemble climate model forecasts produced by the National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) are bias corrected, downscaled to 1/8° horizontal resolution, and disaggregated to a daily time step for input to the Variable Infiltration Capacity hydrologic model. Bias correction is effected by evaluating the GSM ensemble forecast variables as percentiles relative to the GSM model climatology and then extracting the percentiles\u27 associated variable values instead from the observed climatology. The monthly meteorological forecasts are then interpolated to the finer hydrologic model scale, at which a daily signal that preserves the forecast anomaly is imposed through resampling of the historic record. With the resulting monthly runoff and streamflow forecasts for the East Coast and Ohio River basin, we evaluate the bias correction and resampling approaches during the southeastern United States drought from May to August 2000 and also for the El Niño conditions of December 1997 to February 1998. For the summer 2000 study period, persistence in anomalous initial hydrologic states predominates in determining the hydrologic forecasts. In contrast, the El Niño-condition hydrologic forecasts derive direction both from the climate model forecast signal and the antecedent land surface state. From a qualitative standpoint the hydrologic forecasting strategy appears successful in translating climate forecast signals to hydrologic variables of interest for water management

Using synoptic-scale land surface moisture indices for hydrologic prediction

Forecasting seasonal runoff is an important challenge in the western U.S. because the timing and volume of summer (dry season) streamflow plays a critical role in managing water supply and delivery systems. Beginning in December of each year, observations of the snow pack begin to provide invaluable information on the amount of runoff expected in late spring and summer. The skill of such water supply forecasts also benefits, in some locations, from the consideration of synoptic climate indices -- e.g., using sea surface temperature anomalies to characterize ENSO or PDO state, which have been shown to have predictable teleconnections to land surface hydrology months in advance. These two sources of forecast information complement each other, with climate/SST state information providing predictability at long lead times (3-9 months), and observed snow and other moisture states of local catchments providing skill at shorter lead times (1-4 months). However, because similar SST anomalies can produce widely different future hydrology outcomes, information at intermediate lead times describing how a particular teleconnection or climate pattern is evolving on a macro-scale level (especially regarding precipitation anomalies) may provide additional insight into future spring and summer runoff. In this study, we explore the use of a long-term land surface hydrology data set to define continentalscale hydrologic indices with potential predictive value, and to assess where in the western U.S. these might enhance predictability already achievable using local observations and climate indices. We find that the west-wide hydrologic indices have potential value in the Pacific Northwest and the Southwest, but yield little improvement in other areas of the western U.S., relative to existing climate and observation-based indices

Wave Turbulence and Energy Cascade in the Hippocampus

Mesoscale cortical activity can be defined as the organization of activity of large neuron populations into collective action, forming time-dependent patterns such as traveling waves. Although collective action may play an important role in the cross-scale integration of brain activity and in the emergence of cognitive behavior, a comprehensive formulation of the laws governing its dynamics is still lacking. Because collective action processes are macroscopic with respect to neuronal activity, these processes cannot be described directly with methods and models developed for the microscale (individual neurons).To identify the characteristic features of mesoscopic dynamics, and to lay the foundations for a theoretical description of mesoscopic activity in the hippocampus, we conduct a comprehensive examination of observational data of hippocampal local field potential (LFP) recordings. We use the strong correlation between rat running-speed and the LFP power to parameterize the energy input into the hippocampus, and show that both the power and non-linearity of collective action (e.g., theta and gamma rhythms) increase with increased speed. Our results show that collective-action dynamics are stochastic (the precise state of a single neuron is irrelevant), weakly non-linear, and weakly dissipative. These are the principles of the theory of weak turbulence. Therefore, we propose weak turbulence a theoretical framework for the description of mesoscopic activity in the hippocampus. The weak turbulence framework provides a complete description of the cross-scale energy exchange (the energy cascade). It uncovers the mechanism governing major features of LFP spectra and bispectra, such as the physical meaning of the exponent α of power-law LFP spectra (e.g., f−α, where f is the frequency), the strengthening of theta-gamma coupling with energy input into the hippocampus, as well as specific phase lags associated with their interaction. Remarkably, the weak turbulence framework is consistent with the theory of self organized criticality, which provides a simple explanation for the existence of the power-law background spectrum. Together with self-organized criticality, weak turbulence could provide a unifying approach to modeling the dynamics of mesoscopic activity

Projected climate-induced faunal change in the western hemisphere

Climate change is predicted to be one of the greatest drivers of ecological change in the coming century. Increases in temperature over the last century have clearly been linked to shifts in species distributions. Given the magnitude of projected future climatic changes, we can expect even larger range shifts in the coming century. These changes will, in turn, alter ecological communities and the functioning of ecosystems. Despite the seriousness of predicted climate change, the uncertainty in climate-change projections makes it difficult for conservation managers and planners to proactively respond to climate stresses. To address one aspect of this uncertainty, we identified predictions of faunal change for which a high level of consensus was exhibited by different climate models. Specifically, we assessed the potential effects of 30 coupled atmosphere–ocean general circulation model (AOGCM) future-climate simulations on the geographic ranges of 2954 species of birds, mammals, and amphibians in the Western Hemisphere. Eighty percent of the climate projections based on a relatively low greenhouse-gas emissions scenario result in the local loss of at least 10% of the vertebrate fauna over much of North and South America. The largest changes in fauna are predicted for the tundra, Central America, and the Andes Mountains where, assuming no dispersal constraints, specific areas are likely to experience over 90% turnover, so that faunal distributions in the future will bear little resemblance to those of today

Experience-Dependent Effects of Muscimol-Induced Hippocampal Excitation on Mnemonic Discrimination

Memory requires similar episodes with overlapping features to be represented distinctly, a process that is disrupted in many clinical conditions as well as normal aging. Data from humans have linked this ability to activity in hippocampal CA3 and dentate gyrus (DG). While animal models have shown the perirhinal cortex is critical for disambiguating similar stimuli, hippocampal activity has not been causally linked to discrimination abilities. The goal of the current study was to determine how disrupting CA3/DG activity would impact performance on a rodent mnemonic discrimination task. Rats were surgically implanted with bilateral guide cannulae targeting dorsal CA3/DG. In Experiment 1, the effect of intra-hippocampal muscimol on target-lure discrimination was assessed within subjects in randomized blocks. Muscimol initially impaired discrimination across all levels of target-lure similarity, but performance improved on subsequent test blocks irrespective of stimulus similarity and infusion condition. To clarify these results, Experiment 2 examined whether prior experience with objects influenced the effect of muscimol on target-lure discrimination. Rats that received vehicle infusions in a first test block, followed by muscimol in a second block, did not show discrimination impairments for target-lure pairs of any similarity. In contrast, rats that received muscimol infusions in the first test block were impaired across all levels of target-lure similarity. Following discrimination tests, rats from Experiment 2 were trained on a spatial alternation task. Muscimol infusions increased the number of spatial errors made, relative to vehicle infusions, confirming that muscimol remained effective in disrupting behavioral performance. At the conclusion of behavioral experiments, fluorescence in situ hybridization for the immediate-early genes Arc and Homer1a was used to determine the proportion of neurons active following muscimol infusion. Contrary to expectations, muscimol increased neural activity in DG. An additional experiment was carried out to quantify neural activity in naïve rats that received an intra-hippocampal infusion of vehicle or muscimol. Results confirmed that muscimol led to DG excitation, likely through its actions on interneuron populations in hilar and molecular layers of DG and consequent disinhibition of principal cells. Taken together, our results suggest disruption of coordinated neural activity across the hippocampus impairs mnemonic discrimination when lure stimuli are novel

Generation of Mie Size Microdroplet Aerosols with Applications in Laser-Driven Fusion Experiments

We have developed a tunable source of Mie scale microdroplet aerosols that can be used for the generation of energetic ions. To demonstrate this potential, a terawatt Ti:Al2O3 laser focused to 2×1019 W/cm2 was used to irradiate heavy water (D2O) aerosols composed of micron-scale droplets. Energetic deuterium ions, which were generated in the laser-droplet interaction, produced deuterium-deuterium fusion with approximately 2×103 fusion neutrons measured per joule of incident laser energy

Coexistence of carbonyl and ether groups on oxygen-terminated (110)-oriented diamond surfaces

Diamond-based materials have unique properties that are exploited in many electrochemical, optical, thermal, and quantum applications. When grown via chemical vapor deposition (CVD), the growth rate of the (110) face is typically much faster than the other two dominant crystallographic orientations, (111) and (100). As such, achieving sufficiently large-area and high-quality (110)-oriented crystals is challenging and typically requires post-growth processing of the surface. Whilst CVD growth confers hydrogen terminations on the diamond surface, the majority of post-growth processing procedures render the surface oxygen-terminated, which in turn impacts the surface properties of the material. Here, we determine the oxygenation state of the (110) surface using a combination of density functional theory calculations and X-ray photoelectron spectroscopy experiments. We show that in the 0–1000 K temperature range, the phase diagram of the (110) surface is dominated by a highly stable phase of coexisting and adjacent carbonyl and ether groups, while the stability of peroxide groups increases at low temperatures and high pressures. We propose a mechanism for the formation of the hybrid carbonyl-ether phase and rationalize its high stability. We further corroborate our findings by comparing simulated core-level binding energies with experimental X-ray photoelectron spectroscopy data on the highest-quality (110)-oriented diamond crystal surface reported to date

Longer fixation duration while viewing face images

The spatio-temporal properties of saccadic eye movements can be influenced by the cognitive demand and the characteristics of the observed scene. Probably due to its crucial role in social communication, it is argued that face perception may involve different cognitive processes compared with non-face object or scene perception. In this study, we investigated whether and how face and natural scene images can influence the patterns of visuomotor activity. We recorded monkeys’ saccadic eye movements as they freely viewed monkey face and natural scene images. The face and natural scene images attracted similar number of fixations, but viewing of faces was accompanied by longer fixations compared with natural scenes. These longer fixations were dependent on the context of facial features. The duration of fixations directed at facial contours decreased when the face images were scrambled, and increased at the later stage of normal face viewing. The results suggest that face and natural scene images can generate different patterns of visuomotor activity. The extra fixation duration on faces may be correlated with the detailed analysis of facial features

Proteomics: in pursuit of effective traumatic brain injury therapeutics

Effective traumatic brain injury (TBI) therapeutics remain stubbornly elusive. Efforts in the field have been challenged by the heterogeneity of clinical TBI, with greater complexity among underlying molecular phenotypes than initially conceived. Future research must confront the multitude of factors comprising this heterogeneity, representing a big data challenge befitting the coming informatics age. Proteomics is poised to serve a central role in prescriptive therapeutic development, as it offers an efficient endpoint within which to assess post-TBI biochemistry. We examine rationale for multifactor TBI proteomic studies and the particular importance of temporal profiling in defining biochemical sequences and guiding therapeutic development. Lastly, we offer perspective on repurposing biofluid proteomics to develop theragnostic assays with which to prescribe, monitor and assess pharmaceutics for improved translation and outcome for TBI patients