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The Alternative Fueling Stations dataset is updated daily from the National Renewable Energy Laboratory (NREL) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). For more information about the update cycle and data collection methods, please refer to https://afdc.energy.gov/stations/#/find/nearest?show_about=true. This dataset shows all station access types (public and private) and statuses (available, planned, and temporarily unavailable) by default. To view only publicly available stations, use the access and status filters. The U.S. Department of Energy collects these data in partnership with Clean Cities coalitions and their stakeholders to help fleets and consumers find alternative fueling stations. Clean Cities coalitions foster the nation's economic, environmental, and energy security by working locally to advance affordable, efficient, and clean transportation fuels and technologies. This data can be found on the Alternative Fuels Data Center: https://doi.org/10.21949/1519144. For more information about the data schema and data dictionary, please see https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/all/#response-fields. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529008

This data package contains the Merit-Based Incentive Payments System (MIPS) Data. It also includes the Alternative Payment Models (APMs) that CMS (Centers for Medicare & Medicaid Services) operates.

Alternative fueling stations are located throughout the United States and Canada, and their availability continues to grow. The Alternative Fuels Data Center (AFDC) maintains a website where you can find alternative fueling stations near you or on a route, obtain counts of alternative fueling stations by state, view maps, and more. The most recent dataset available for download here provides a "snapshot" of the alternative fueling station information for compressed natural gas (CNG), ethanol (E85), propane/liquefied petroleum gas (LPG), biodiesel (B20 and above), electric vehicle charging, hydrogen, and liquefied natural gas (LNG), as of July 29, 2021.

Enzyme detection in liquid samples is a complex laboratory procedure, based on assays that are generally time- and cost-consuming, and require specialized personnel. Surface acoustic wave sensors can be used for this application, overcoming the cited limitations. To give our contribution, in this work we present the bottom-up development of a surface acoustic wave biosensor to detect active α-glycosidase in aqueous solutions. Our device, optimized to work at an ultra-high frequency (around 740 MHz), is functionalized with a newly synthesized probe 7-mercapto-1-eptyl-D-maltoside, bringing one maltoside terminal moiety. The probe is designed ad hoc for this application and tested in-cuvette to analyze the enzymatic conversion kinetics at different times, temperatures and enzyme concentrations. Preliminary data are used to optimize the detection protocol with the SAW device. In around 60 min, the SAW device is able to detect the enzymatic conversion of the maltoside unit into glucose in the presence of the active enzyme. We obtained successful α-glycosidase detection in the concentration range 0.15–150 U/mL, with an increasing signal in the range up to 15 U/mL. We also checked the sensor performance in the presence of an enzyme inhibitor as a control test, with a signal decrease of 80% in the presence of the inhibitor. The results demonstrate the synergic effect of our SAW Lab-on-a-Chip and probe design as a valid alternative to conventional laboratory tests. Methods UV data were acquired via UV-Vis spectroscopy with a JASCO V550 spectrophotometer (JASCO Europe, Cremello, Italy); the raw data were used without any further processing. QCM data were acquired with a QCM-D E4 model (Q-Sense AB, Sweden); the raw data were processed by MATLAB to eliminate any offset or drift. SAW-LoC data were acquired with a vectorial network analyzer E5071C (Agilent Technologies) connected to an RF switch 34980A (Agilent Technologies); an in-house software based on LabView® is used to pilot the RF-switch and the vectorial network analyzer.

Learn about the advantages and challenges of lab grown meat, a technology that provides an alternative source for meat products without the need for animal breeding, feeding, or slaughtering. Discover how lab grown meat is environmentally-friendly, sustainable, and cruelty-free, and how it has the potential to revolutionize the meat industry.

1. Sharks, rays and their cartilaginous relatives (Class Chondricthyes, herein 'sharks') are amongst the world's most threatened species groups, primarily due to overfishing, which in turn is driven by complex market forces including demand for fins. Understanding the high-value shark fin market is a global priority for conserving shark and rays, yet the preferences of shark fin consumers are not well understood. This gap hinders the design of evidence-based consumer-focused conservation interventions.
2. Using an online discrete choice experiment, we explored preferences for price, quality, size, menu types (as a proxy for exclusivity) and source of fins (with varying degrees of sustainability) among 300 shark fin consumers in Singapore: a global entrepot for shark fin trade.
3. Overall, consumers preferred lower-priuced fins sourced from responsible fisheries or produced using novel lab-cultured techniques. We also identified four consumer segments, each with distinct psychographics characteristics and consumption behaviors.
4. These preferences and profiles could be leveraged to inform new regulatory and market-based interventions regarding the sale and consumption of shark fins, and incentivize responsible fisheries and lab-cultured innovation for delivering conservation and sustainability goals.
5. In addition, message framing around health benefits, shark endangerment and counterfeiting could reinforce existing beliefs amongst consumers in Singapore and drive behavioral shifts to ensure that market demand remains within the limits of sustainable supply.

This dataset includes all the responses collected from the online discrete choice experiment which was implemented by a market survey company, as well as the goodness-of-fit chi-square analyses. These data were also used to plot the figures in the manuscript and the Supplemental Information. Password for excel sheet titled 'Final CEOE data' is 35433. Please refer to the published manuscript for more detailed information.

The authors received financial support from Silverstrand Capital awarded to Wildlife Conservation Society for the research, authorship, and publication of this work.

This dataset is the master file of the Pillbox database of the US National Library of Medicine (NLM), which contains descriptive information on the existing medication pills in the market. Values supplied by the drug companies to FDA (Food and Drug Administration) as part of the Structured Product Labels are labeled "SPL". All other values are added by the National Library of Medicine’s (NLM) Pillbox or RxNorm datasets.

ObjectiveValidate the performance characteristics of two analyte specific, laboratory developed tests (LDTs) for the quantification of SARS-CoV-2 subgenomic RNA (sgRNA) and viral load on the Hologic Panther Fusion® using the Open Access functionality.MethodsCustom-designed primers/probe sets targeting the SARS-CoV-2 Envelope gene (E) and subgenomic E were optimized. A 20-day performance validation following laboratory developed test requirements was conducted to assess assay precision, accuracy, analytical sensitivity/specificity, lower limit of detection and reportable range.ResultsQuantitative SARS-CoV-2 sgRNA (LDT-Quant sgRNA) assay, which measures intermediates of replication, and viral load (LDT-Quant VLCoV) assay demonstrated acceptable performance. Both assays were linear with an R2 and slope equal to 0.99 and 1.00, respectively. Assay precision was evaluated between 4–6 Log10 with a maximum CV of 2.6% and 2.5% for LDT-Quant sgRNA and LDT-Quant VLCoV respectively. Using negative or positive SARS-CoV-2 human nasopharyngeal swab samples, both assays were accurate (kappa coefficient of 1.00 and 0.92). Common respiratory flora and other viral pathogens were not detected and did not interfere with the detection or quantification by either assay. Based on 95% detection, the assay LLODs were 729 and 1206 Copies/mL for the sgRNA and VL load LDTs, respectively.ConclusionThe LDT-Quant sgRNA and LDT-Quant VLCoV demonstrated good analytical performance. These assays could be further investigated as alternative monitoring assays for viral replication; and thus, medical management in clinical settings which could inform isolation/quarantine requirements.