Accurate soil moisture estimation is essential for agriculture, disaster management, and hydrology. Traditional methods are accurate but time-consuming and destructive, while existing remote sensing approaches lack the temporal resolution required for near-real-time applications. In this study, a near-real-time multi-depth soil moisture prediction methodology has been developed using surface temperature data from the International Soil Moisture Network stations, along with other static (topography, soil texture) and dynamic (hydrometeorological) variables that can be obtained at minimal latency. An eXtreme Gradient Boosting (XGB) model was trained to predict soil moisture and validated, using land surface temperature (LST) from Landsat-8, Landsat-9, and the Ecosystem Spaceborne Thermal Radiometer Experiment on the International Space Station (ECOSTRESS) datasets. Testing set performance (Pearson r, r: 0.901 - 0.937; unbiased Root Mean Squared Error, ubRMSE: 0.038 - 0.047 m3/m3) across 5, 10, and 20 cm depths indicated low prediction error and minimal overfitting. Validation at training locations using Landsat (r: 0.879 - 0.921; ubRMSE; 0.032 - 0.043 m³/m³) and ECOSTRESS (r: 0.888 - 0.914; ubRMSE: 0.031 - 0.039 m³/m³) confirmed good performance across spatial and temporal scales. Validation at independent locations using Landsat (r: 0.631 - 0.707; ubRMSE: 0.065 - 0.074 m³/m³) and ECOSTRESS (r: 0.620 - 0.724; ubRMSE: 0.057 - 0.076 m³/m³) demonstrated transferable predictive capability. These results establish that satellite-derived LST, combined with ancillary variables available at low latency, enables near-real-time soil moisture prediction across multiple depths and independent sites, with direct applicability to operational monitoring contexts.

Despite the decreased disaster resilience of rural communities in the Great Lakes region to flooding, flood mitigation efforts have been impeded by inadequate data and lack of appropriate tools for understanding flood risk. Development of such resources often requires data and computationally intensive approaches, which are challenging in data-scarce conditions. This study presents the development of a web application in Google Earth Engine (GEE) for flood risk assessment. The application utilizes the Height Above the Nearest Drainage (HAND) model and synthetic rating curve (SRC) for fluvial flood inundation modeling, the Simulating WAves Nearshore (SWAN) model for coastal flood inundation modeling, the United States Geological Survey (USGS) regional regression equations for estimating peak discharge, and depth-damage functions of the HAZUS-MH flood model for estimating losses due to building-level impacts. The GEE-based geospatial web application, which is operational across five counties in the Western Upper Peninsula (WUP) of Michigan, fulfills the requirement of the community and decision-makers to assess the risks caused by flooding in the region. We demonstrated the applicability of the tool in the Ontonagon River, Michigan, and the results indicate the suitability of the platform for implementing decisions, long-term planning, and understanding flood risk with a reasonable degree of accuracy.

In response to record-high water levels in the Great Lakes, there has been a notable surge in engineering interventions and the construction of armoring structures to mitigate shoreline erosion. However, the efficacy of these defensive measures against erosion and their broader implications for the physical vulnerability of coastal communities remain critical concerns. Our pilot study applied the Coastal Vulnerability Index (CVI) method to the Muskegon shoreline, enhancing it by calculating CVI values for individual parcels and integrating the shoreline rate of change and shoreline armaments. This approach localized variations and provided a precise understanding of factors influencing vulnerability. We found that using the shoreline rate of change allowed us to identify vulnerable areas prone to erosion due to dynamic shoreline processes and seasonal variations. In the study, seasonality significantly influenced vulnerability, particularly through ice cover, which aligns with findings on seasonal shoreline erosion risks from previous studies. It also underscores the importance of considering temporal dynamics in assessing coastal vulnerability in the Great Lakes region. We observed higher vulnerability in the northern and southern parts of the county's shoreline compared to the central areas. Sites near heavily armored properties exhibited increased vulnerability, highlighting the complex impacts of shoreline armors on adjacent areas. The developed CVI holds the promise of providing coastal managers with invaluable insights. Specifically, it guides the reclassification of high-vulnerability areas and informs the formulation of policies that address the multifaceted challenges associated with shoreline armoring.

[This corrects the article DOI: 10.1021/jacsau.4c00686.].

Soil texture identification is vital for various agricultural and engineering applications but generally involves rigorous laboratory work, especially for estimating USCS (Unified Soil Classification System) soil texture classes. Soil texture influences soil water storage capacity, soil fertility, compaction characteristics, and soil strength. Soil spectroscopy offers a reliable approach that is non-destructive, rapid, and cost-effective to estimate several soil properties including texture. For engineering applications, the USCS soil texture classes are preferred, but very few studies have focussed on estimating USCS soil texture using soil spectroscopy or remote sensing data in general. Two large soil spectral libraries (SSLs), viz., Kellog Soil Spectral Library (KSSL) and Open-source Soil Spectral Library (OSSL), as well as three deep learning algorithms (VGG-16, ResNet-16, and Swin transformers), were used in this study to predict six USCS soil texture classes and three USCS soil texture groups. The USCS soil texture classes and groups were derived by grouping clay, sand, and silt fractions that are closely associated with the corresponding USCS soil texture classes. The results indicate that the Swin transformer model performed the best with an accuracy of 67

The European Commission plans to reduce pesticides and fertilizer use in agriculture substantially under its Green Deal's Farm to Fork strategy. Policy alternatives are needed to overcome these input reductions, for instance by allocating R&D towards commodities most affected by climage change and improving the precision of input use. Since climate change is likely to impact agriculture negatively, productivity measures that account for all inputs and climate change impacts can help to weigh such policy responses. We adopt U.S. farm-level data and a multi-output and multi-input production technology to estimate relative farm productivity performances with climatic effects. Exploiting long-term weather patterns, we find comparative advantages among some crop farms and ample productivity improvement potential across all of them.

Significance: Nonhealing wounds are an ever-growing global pandemic, with mortality rates and management costs exceeding many common cancers. Although our understanding of the molecular and cellular factors driving wound healing continues to grow, standards for diagnosing and evaluating wounds remain largely subjective and experiential, whereas therapeutic strategies fail to consistently achieve closure and clinicians are challenged to deliver individualized care protocols. There is a need to apply precision medicine practices to wound care by developing evidence-based approaches, which are predictive, prescriptive, and personalized. Recent Advances: Recent developments in "advanced" wound diagnostics, namely biomarkers (proteases, acute phase reactants, volatile emissions, and more) and imaging systems (ultrasound, autofluorescence, spectral imaging, and optical coherence tomography), have begun to revolutionize our understanding of the molecular wound landscape and usher in a modern age of therapeutic strategies. Herein, biomarkers and imaging systems with the greatest evidence to support their potential clinical utility are reviewed. Critical Issues: Although many potential biomarkers have been identified and several imaging systems have been or are being developed, more high-quality randomized controlled trials are necessary to elucidate the currently questionable role that these tools are playing in altering healing dynamics or predicting wound closure within the clinical setting. Future Directions: The literature supports the need for the development of effective point-of-care wound assessment tools, such as a platform diagnostic array that is capable of measuring multiple biomarkers at once. These, along with advances in telemedicine, synthetic biology, and "smart" wearables, will pave the way for the transformation of wound care into a precision medicine. Clinical Trial Registration number: NCT03148977.

Promiscuous activity of a glycosyltransferase was exploited to polymerise glucose from UDP-glucose via the generation of β-1,4-glycosidic linkages. The biocatalyst was incorporated into biocatalytic cascades and chemo-enzymatic strategies to synthesise cello-oligosaccharides with tailored functionalities on a scale suitable for employment in mass spectrometry-based assays. The resulting glycan structures enabled reporting of the activity and selectivity of celluloltic enzymes.

The β-1,4-glucose linkage of cellulose is the most abundant polymeric linkage on earth and as such is of considerable interest in biology and biotechnology. It remains challenging to synthesize this linkage in vitro due to a lack of suitable biocatalysts; the natural cellulose biosynthetic machinery is a membrane-associated complex with processive activity that cannot be easily manipulated to synthesize tailor-made oligosaccharides and their derivatives. Here we identify a promiscuous activity of a soluble recombinant biocatalyst, Neisseria meningitidis glycosyltransferase LgtB, suitable for the polymerization of glucose from UDP-glucose via the generation of β-1,4-glycosidic linkages. We employed LgtB to synthesize natural and derivatized cello-oligosaccharides and we demonstrate how LgtB can be incorporated in biocatalytic cascades and chemo-enzymatic strategies to synthesize cello-oligosaccharides with tailored functionalities. We also show how the resulting glycan structures can be applied as chemical probes to report on activity and selectivity of plant cell wall degrading enzymes, including lytic polysaccharide monooxygenases. We anticipate that this biocatalytic approach to derivatized cello-oligosaccharides via glucose polymerization will open up new applications in biology and nanobiotechnology.

Background The STI epidemic continues to grow among young people. Encouraging screening and careful partner selection are approaches to controlling STI transmission. Methods The SAFE App is a free, phone-based program that encourages users to undergo regular testing for STIs. The app allows for the real-time collection of data concerning the frequency of testing among various demographic populations. Users may securely upload test results from their regular healthcare provider or arrange to be tested through the app. Users who test through the app can use health insurance or pay a flat fee to test without generating an insurance claim. Test results can be securely displayed on the user’s phone so they have the ability to easily share them with potential sex partners. The app is available for both Apple and Android systems and is supported by a network of physicians who communicate results to users and arrange for treatment for any user with a positive result. State reporting requirements are also fulfilled through the app. Results To date, a limited number of users have registered for the app including about 200 from 38 states who imported results into the app and about 150 from 22 states who obtained testing through the app. Among the latter, testing was completed at a commercial laboratory and the result electronically transferred to the SAFE medical record for rapid review by a physician and release to the user. Some users reported that they had never had an STI screen prior to testing through the app. After completion of scalability testing, the app will be promoted through social media. Conclusion Widespread use of this app should encourage more testing of at-risk populations, generate demographic data concerning the frequency of STI testing, as well as ultimately reducing the spread of STIs by more judicious partner selection. Disclosure No significant relationships.

Building historical geographic information system (HGIS) datasets is time consuming and very expensive, especially when built at the scales that permit analysis of the lived experiences of individuals or the morphology of buildings or streets. Further, these datasets are often built exclusively in the academy, with little input from the contemporary communities they represent. In this paper, we review the use of the public in crowdsourcing historical data creation, and using the Keweenaw Time Traveler set in Michigan's Copper Country as a case study, we call for a new approach to HGIS scholarship that includes a robust public partnership to building HGIS datasets. The creation of a public participatory HGIS approach to HGIS scholarship can increase efficiencies of, public relevance in, and extend the reach of, HGIS projects beyond the academy. We have established a set of best practices that include, incorporating the public in the HGIS interface design, providing immediate public data access, contextualization of spatial data in space-time, comprehensive public history outreach in person and online, and creating affordances for the public to contribute their own historical spatial knowledge through spatial storytelling. Together, these activities can promote the long-term sustainability and success of historical data crowdsourcing projects.

Multiple sets of heuristic have been developed and studied in the Human Computer Interaction (HCI) domain as a method for fast, lightweight evaluations for usability problems. However, none of the heuristics have been adopted by the information visualization or the visual analytics communities. Our literature review looked at heuristic sets developed by Nielsen and Molich [7] and Forsell and Johansson [1] to understand how these heuristics were developed and their intended applications. We also reviewed heuristic studies conducted by Hearst and colleagues [2] and Väätäjä and colleagues [10] to determine how individuals apply heuristics to evaluating visualization systems. While each study noted potential issues with the heuristic descriptions and the evaluator’s familiarity with the heuristics, no direct connections were made. Our research looks to understand how individuals with domain expertise in information visualization and visual analytics could use heuristics to discover usability problems and evaluate visualizations. By empirically evaluating visualization heuristics, we can identify the key ways that these heuristics can be used to inform the visual analytics design process. Further, they may help to identify usability problems that are and are not task specific. We hope to use this process to also identify missing heuristics that may apply to designs for different analytic purposes.

This paper employs a stochastic frontier approach to examine how climate change and extreme weather affect U.S. agricultural productivity using 1940-1970 historical weather data (mean and variation) as the norm.We have four major findings.First, using temperature humidity index (THI) load and Oury index for the period 1960-2010 we find each state has experienced different patterns of climate change in the past half century, with some states incurring drier and warmer conditions than others.Second, the higher the THI load (more heat waves) and the lower the Oury index (much drier) will tend to lower a state's productivity.Third, the impacts of THI load shock and Oury index shock variables (deviations from historical norm fluctuations) on productivity are more robust than the level of THI and Oury index variables across specifications.Fourth, we project potential impacts of climate change and extreme weather on U.S. regional productivity based on the estimates.We find that the same degree changes in temperature or precipitation will have uneven impacts on regional productivities, with Delta, Northeast, and Southeast regions incurring much greater effects than other regions, using 2000-2010 as the reference period.

The potential for future cost reductions in wind power affects adoption and support policies. Prior analyses of cost reductions give inconsistent results. The learning rate, or fractional cost reduction per doubling of production, ranges from −3% to +33% depending on the study. This lack of consensus has, we believe, contributed to high variability in forecasts of future costs of wind power. We find that learning rate can be very sensitive to the starting and ending years of datasets and the geographical scope of the study. Based on a single factor experience curve that accounts for capacity factor gains, wind quality decline, and exogenous shifts in capital costs, we develop an improved model with reduced temporal variability. Using a global adoption model, the wind-learning rate is between 7.7% and 11%, with a preferred estimate of 9.8%. Using global scenarios for future wind deployment, this learning rate range implies that the cost of wind power will decline from 5.5 cents/kWh in 2015 to 4.1–4.5 cents/kWh in 2030, lower than a number of other forecasts. If attained, wind power may be the cheapest form of new electricity generation by 2030, suggesting that support and investment in wind should be maintained or expanded.

AbstractPalladium(II) in combination with a monodentate phosphine ligand enables the unprecedented direct and α‐stereoselective catalytic synthesis of deoxyglycosides from glycals. Initial mechanistic studies suggest that in the presence of N‐phenyl‐2‐(di‐tert‐butylphosphino)pyrrole as the ligand, the reaction proceeds via an alkoxy palladium intermediate that increases the proton acidity and oxygen nucleophilicity of the alcohol. The method is demonstrated with a wide range of glycal donors and acceptors, including substrates bearing alkene functionalities.

Carbohydrates play a pivotal role in biological systems and present an opportunity to develop potent carbohydrate-derived therapeutics and diagnostic tools for the treatment and detection of disease. To better comprehend the biological functions of carbohydrates, access to pure and structurally defined oligosaccharides is needed. Oligosaccharides are commonly synthesized by chemical means; however, despite progress in glycosylation chemistry, the efficient and stereoselective formation of glycosidic bonds by mild, nontoxic and low-cost methods remains a challenge. Organocatalysis is an exciting field that utilizes small organic molecules to effect chemical transformation where traditionally transition metal catalysis would be required. This microreview presents recent advances in the application of organocatalysis to carbohydrate chemistry and in particular to the stereoselective synthesis of oligosaccharides.

BACKGROUND It has been nearly a decade since findings revealed that a sample of U.S. nurses routinely used only 30 physical assessment techniques in clinical practice. In a time of differentiating nice-to-know from need-to-know knowledge and skills, what has changed in nursing education? METHOD This cross-sectional, descriptive study examines the physical assessment skills taught and used among nursing students at one baccalaureate nursing education program located in the midwestern United States. RESULTS Findings highlight the similarities and differences from previous studies and offer insight as to how closely nursing education mirrors the skills needed for clinical practice. CONCLUSION Nurse educators must continue to discriminate content taught in prelicensure nursing education programs and should consider the attainment of competency of those essential skills that most lend to optimal patient outcomes. [J Nurs Educ. 2017;56(5):287-291.].

Cognitive Interpretation is a method of understanding geology before interpreting seismic that harnesses our natural cognitive capabilities. Cognitive Interpretation combines the power of algorithmic computation within software with the benefits of an interpreter’s knowledge and experience. About 40% of the brain is devoted to visual cognition and there are strong links between visual system and memory. Because of this, the brain effectively links current visual data with past experience and learnings in order to make sense of incomplete or ambiguous data. This linking of visual data and past learnings is what seismic interpreters do with data, by pattern matching what is seen with what geological features are known to look like. In this study, reflectivity data was directly translated into geological information, before any traditional line-by-line interpretation. Presentation Date: Wednesday, October 19, 2016 Start Time: 2:45:00 PM Location: 140 Presentation Type: ORAL

We measure corn and total agricultural area response to the biofuels boom in the United States from 2006 to 2010. Specifically, we use newly available micro-scale grid cell data to test whether a location's corn and total agricultural cultivation rose in response to the capacity of ethanol refineries in their vicinity. Based on these data, acreage in corn and overall agriculture not only grew in already-cultivated areas but also expanded into previously uncultivated areas. Acreage in corn and total agriculture also correlated with proximity to ethanol plants, though the relationship dampened over the time period. A formal estimation of the link between acreage and ethanol refineries, however, must account for the endogenous location decisions of ethanol plants and areas of corn supply. We present historical evidence to support the use of the US railroad network as a valid instrument for ethanol plant locations. Our estimates show that a location's neighborhood refining capacity exerts strong and significant effects on acreage planted in corn and total agricultural acreage. The largest impacts of ethanol plants were felt in locations where cultivation area was relatively low. This high-resolution evidence of ethanol impacts on local agricultural outcomes can inform researchers and policy-makers concerned with crop diversity, environmental sustainability, and rural economic development.