A Novel Technique for the Simultaneous Collection of Reflection and Transmission Data from Thin Films in the Extreme Ultraviolet

Studies of thin films in the Extreme Ultraviolet (EUV) are difficult given that most materials readily absorb photons of these energies. By depositing a thin film of the material of interest on a silicon photodiode, transmission measurements can be made throughout the EUV. If the measurements are made in a range of low absorption, the extinction coefficient, k, can be found with relative ease. However, if the material’s absorption is considerable, reflection measurements are needed to supplement the transmission data in order to find the optical constants n and k. The technique developed allows for reflection and transmission measurements to be taken simultaneously, which combined, account for all of the measurable photons from the original beam: (those which cannot be counted are photons absorbed into the thin film material). Also, the technique presented allows for data to be collected from practically all angles of incidence. This technique has been applied to a thin film of scandium oxide (d=65 nm), with measurements taken over wavelengths from 2.5-25 nm, and at angles of incidence 12 degrees from grazing to normal

The Effects of Oxidation on the Refractive Index of Uranium Thin Films in the Extreme Ultraviolet

We measured the transmittance and reflectance of two samples in the extreme ultraviolet (XUV) at the Advanced Light Source at Lawrence Berkeley National Laboratory. The samples were prepared with approximately 20 nm of UOx with one reactively sputtered onto a diode, and one allowed to oxidize naturally on an identical diode. Fitting the reflectance data to the Parratt model yielded a more precise thickness of the UOx film. This thickness combined with a simple analysis of the transmission measurements provides estimates for the imaginary part of the index of refraction for UOx at approximately every tenth of a nanometer from about 3 nm to 30 nm with emphasis in the 12- to 13-nm range. Using these values, a first approximation for the real part of the refractive index has also been calculated. These values provide researchers with information for modeling, design, and fabrication of optical systems in the extreme ultraviolet

Ungulate preference for burned patches reveals strength of fire–grazing interaction

The interactions between fire and grazing are widespread throughout fire-dependent landscapes. The utilization of burned areas by grazing animals establishes the fire–grazing interaction, but the preference for recently burned areas relative to other influences (water, topography, etc.) is unknown. In this study, we determine the strength of the fire–grazing interaction by quantifying the influence of fire on ungulate site selection. We compare the preference for recently burned patches relative to the influence of other environmental factors that contribute to site selection; compare that preference between native and introduced ungulates; test relationships between area burned and herbivore preference; and determine forage quality and quantity as mechanisms of site selection. We used two large ungulate species at two grassland locations within the southern Great Plains, USA. At each location, spatially distinct patches were burned within larger areas through time, allowing animals to select among burned and unburned areas. Using fine scale ungulate location data, we estimated resource selection functions to examine environmental factors in site selection. Ungulates preferred recently burned areas and avoided areas with greater time since fire, regardless of the size of landscape, herbivore species, or proportion of area burned. Forage quality was inversely related to time since fire, while forage quantity was positively related. We show that fire is an important component of large ungulate behavior with a strong influence on site selection that drives the fire–grazing interaction. This interaction is an ecosystem process that supersedes fire and grazing as separate factors, shaping grassland landscapes. Inclusion of the fire–grazing interaction into ecological studies and conservation practices of fire-prone systems will aid in better understanding and managing these systems

Real-Time Monitoring of Aluminum Oxidation Through Wide Band Gap MgF2 Layers for Protection of Space Mirrors

Because of its extraordinary and broad reflectivity, aluminum is the only logical candidate for advanced space mirrors that operate deep into the UV. However, aluminum oxidizes rapidly in the air, and even a small amount of oxide (as little as a nanometer) can have a noticeable, detrimental impact on its reflectivity at short wavelengths. Thin films of wide band gap materials like MgF2 have previously been used to protect aluminum surfaces. Here we report the first real-time, spectroscopic ellipsometry (SE) study of aluminum oxidation as a function of MgF2 over layer thickness, which ranged from 0 – 6 nm. SE data analysis was performed vis-à-vis a multilayer optical model that included a thick silicon nitride layer. The optical constants for evaporated aluminum were initially determined using a multi-sample analysis (MSA) of SE data from MgF2 protected and bare Al surfaces. Two models were then considered for analyzing the real-time data obtained from Al/MgF2 stacks. The first used the optical constants of aluminum obtained in the MSA with two adjustable parameters: the thicknesses of the aluminum and aluminum oxide layers. The thicknesses obtained from this model showed the expected trends (increasing Al2O3 layer thickness and decreasing Al layer thickness with time), but some of the Al2O3 thicknesses were unphysical (negative). Because the optical constants of very thin metals films depend strongly on their structures and deposition conditions, a second, more advanced model was employed that fit the optical constants for Al, and also the Al and Al2O3 thicknesses, for each data set. In particular, the Al and Al2O3 thicknesses and optical constants of Al were determined in an MSA for each of 50 evenly spaced analyses in each four-hour dynamic run performed. The resulting optical constants for Al were then fixed for that sample and the thicknesses of the Al and Al2O3 layers were determined. While the first and second models yielded similar Al and Al2O3 thickness vs. time trends, the film thicknesses obtained in this manner were more physically reasonable. Thicker MgF2 layers slow the oxidation rate of aluminum. The results from this work should prove useful in protecting space mirrors prior to launch

Beyond Inventories: Emergence of a New Era in Rangeland Monitoring

In the absence of technology-driven monitoring platforms, US rangeland policies, management practices, and outcome assessments have been primarily informed by the extrapolation of local information from national-scale rangeland inventories. A persistent monitoring gap between plot-level inventories and the scale at which rangeland assessments are conducted has required decision makers to fill data gaps with statistical extrapolations or assumptions of homogeneity and equilibrium. This gap is now being bridged with spatially comprehensive, annual, rangeland monitoring data across all western US rangelands to as- sess vegetation conditions at a resolution appropriate to inform cross-scale assessments and decisions. In this paper, 20-yr trends in plant functional type cover are presented, confirming two widespread national rangeland resource concerns: widespread increases in annual grass cover and tree cover. Rangeland vegetation monitoring is now available to inform national to regional policies and provide essential data at the scales at which decisions are made and implemented

Spatial Imaging and Screening for Regime Shifts

Screening is a strategy for detecting undesirable change prior to manifestation of symptoms or adverse effects. Although the well-recognized utility of screening makes it commonplace in medicine, it has yet to be implemented in ecosystem management. Ecosystem management is in an era of diagnosis and treatment of undesirable change, and as a result, remains more reactive than proactive and unable to effectively deal with today’s plethora of non-stationary conditions. In this paper, we introduce spatial imaging-based screening to ecology. We link advancements in spatial resilience theory, data, and technological and computational capabilities and power to detect regime shifts (i.e., vegetation state transitions) that are known to be detrimental to human well-being and ecosystem service delivery. With a state-of-the-art landcover dataset and freely available, cloud-based, geospatial computing platform, we screen for spatial signals of the three most iconic vegetation transitions studied in western USA rangelands: (1) erosion and desertification; (2) woody encroachment; and (3) annual exotic grass invasion. For a series of locations that differ in ecological complexity and geographic extent, we answer the following questions: (1) Which regime shift is expected or of greatest concern? (2) Can we detect a signal associated with the expected regime shift? (3) If detected, is the signal transient or persistent over time? (4) If detected and persistent, is the transition signal stationary or non-stationary over time? (5) What other signals do we detect? Our approach reveals a powerful and flexible methodology, whereby professionals can use spatial imaging to verify the occurrence of alternative vegetation regimes, image the spatial boundaries separating regimes, track the magnitude and direction of regime shift signals, differentiate persistent and stationary transition signals that warrant continued screening from more concerning persistent and non-stationary transition signals, and leverage disciplinary strength and resources for more targeted diagnostic testing (e.g., inventory and monitoring) and treatment (e.g., management) of regime shifts. While the rapid screening approach used here can continue to be implemented and refined for rangelands, it has broader implications and can be adapted to other ecological systems to revolutionize the information space needed to better manage critical transitions in nature

VIPR: A probabilistic algorithm for analysis of microbial detection microarrays

<p>Abstract</p> <p>Background</p> <p>All infectious disease oriented clinical diagnostic assays in use today focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly facilitate the highly parallel detection of multiple microbes that may be present in a given clinical specimen. While several algorithms have been described for interpretation of diagnostic microarrays, none of the existing approaches is capable of incorporating training data generated from positive control samples to improve performance.</p> <p>Results</p> <p>To specifically address this issue we have developed a novel interpretive algorithm, VIPR (<b>V</b>iral <b>I</b>dentification using a <b>PR</b>obabilistic algorithm), which uses Bayesian inference to capitalize on empirical training data to optimize detection sensitivity. To illustrate this approach, we have focused on the detection of viruses that cause hemorrhagic fever (HF) using a custom HF-virus microarray. VIPR was used to analyze 110 empirical microarray hybridizations generated from 33 distinct virus species. An accuracy of 94% was achieved as measured by leave-one-out cross validation. <it>Conclusions</it></p> <p>VIPR outperformed previously described algorithms for this dataset. The VIPR algorithm has potential to be broadly applicable to clinical diagnostic settings, wherein positive controls are typically readily available for generation of training data.</p

Challenges of Brush Management Treatment Effectiveness in Southern Great Plains, United States

Woodland expansion is a global challenge documented under varying degrees of disturbance, climate, and land ownership patterns. In North American rangelands, mechanical and chemical brush management practices and prescribed fire are frequently promoted by agencies and used by private landowners to reduce woody plant cover. We assess the distribution of agency-supported cost sharing of brush management (2000−2017) in the southern Great Plains, United States, and evaluate the longevity of treatment application. We test the general expectation that the current brush management paradigm in the southern Great Plains reduces woody plants and conserves rangeland resources at broad scales. This study represents the most comprehensive assessment of treatment longevity following brush management in the southern Great Plains by linking confidential private lands management data to a national inventory program (US Department of Agriculture Natural Resources Conservation Service National Resources Inventory). We observed regional differences in the types of brush management techniques used in cost-sharing programs throughout the study area. Mechanical brush management was the most common practice cost shared in Texas, while a mixture of mechanical and chemical application was most common in Oklahoma. Prescribed fire was most common in Kansas with some areas receiving chemical treatment. Our analysis showed brush management, as implemented, did not reduce tree cover long term and minimally reduced shrub cover. Evidence to support the current brush management paradigm only existed at local site-level scales of analysis (40- to 50-acre area), but treatment effectiveness was short-lived. At regional scales, observed changes in woody plant cover showed little to no overall net reduction from 2000 to 2017. These findings bring into question the philosophy of the current brush management paradigm, its implementation as the default rangeland conservation practice, and its prioritization over alternative practices that prevent new woody plant establishment and enhance resilience of rangelands in the southern Great Plains region

Tracking spatial regimes in animal communities: Implications for resilience-based management

Spatial regimes (the spatial extents of ecological states) exhibit strong spatiotemporal order as they expand or contract in response to retreating or encroaching adjacent spatial regimes (e.g., woody plant invasion of grasslands) and human management (e.g., fire treatments). New methods enable tracking spatial regime boundaries via vegetation landcover data, and this approach is being used for strategic management across biomes. A clear advancement would be incorporating animal community data to track spatial regime boundaries alongside vegetation data. In a 41,170-hectare grassland experiencing woody plant encroachment, we test the utility of using animal community data to track spatial regimes via two hypotheses. (H1) Spatial regime boundaries identified via independent vegetation and animal datasets will exhibit spatial synchrony; specifically, grassland:woodland bird community boundaries will synchronize with grass:woody vegetation boundaries. (H2) Negative feedbacks will stabilize spatial regimes identified via animal data; specifically, frequent fire treatments will stabilize grassland bird community boundaries. We used 26 years of bird community and vegetation data alongside 32 years of fire history data. We identified spatial regime boundaries with bird community data via a wombling approach. We identified spatial regime boundaries with vegetation data by calculating spatial covariance between remotely-sensed grass and woody plant cover per pixel. For fire history data, we calculated the cumulative number of fires per pixel. Setting bird boundary strength (wombling R2 values) as the response variable, we tested our hypotheses with a hierarchical generalized additive model (HGAM). Both hypotheses were supported: animal boundaries synchronized with vegetation boundaries in space and time, and grassland bird communities stabilized as fire frequency increased (HGAM explained 38% of deviance). We can now track spatial regimes via animal community data pixel-by-pixel and year-by-year. Alongside vegetation boundary tracking, tracking animal community boundaries can inform the scale of management necessary to maintain animal communities endemic to desirable ecological states. Our approach will be especially useful for conserving animal communities requiring large-scale, unfragmented landscapes—like grasslands and steppes