Endogenous plant nitroxyl, a new component of nitric oxide biology.

Nitroxyl (HNO), a one-electron reduced and protonated congener of nitric oxide (•NO), was recently discovered in Arabidopsis thaliana. Due to its distinct chemical properties, we believe HNO must be further studied to determine how many physiological processes it impacts.

Quantifying NOx point sources with Landsat and Sentinel-2 satellite observations of NO2 plumes.

We show that the Landsat and Sentinel-2 satellites can detect NO2 plumes from large point sources at 10 to 60 m pixel resolution in their blue and ultrablue bands. We use the resulting NO2 plume imagery to quantify nitrogen oxides (NOx) emission rates for several power plants in Saudi Arabia and the United States, including a 13-y analysis of 132 Landsat plumes from Riyadh power plant 9 from 2009 through 2021. NO2 in the plumes initially increases with distance from the source, likely reflecting recovery from ozone titration. The fine pixel resolutions of Landsat and Sentinel-2 enable separation of individual point sources and stacks, including in urban background, and the long records enable examination of multidecadal emission trends. Our inferred NOx emission rates are consistent with previous estimates to within a precision of about 30%. Sources down to ~500 kg h-1 can be detected over bright, quasi-homogeneous surfaces. The 2009 to 2021 data for Riyadh power plant 9 show a strong summer peak in emissions, consistent with increased power demand for air conditioning, and a marginal slow decrease following the introduction of Saudi Arabia's Ambient Air Standard 2012.

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.

Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA-disease associations (MDAs), miRNA-target interactions (MTIs), and disease-gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.

Tracing sources of atmospheric methane using clumped isotopes.

We apply a recently developed measurement technique for methane (CH4) isotopologues* (isotopic variants of CH4-13CH4, 12CH3D, 13CH3D, and 12CH2D2) to identify contributions to the atmospheric burden from fossil fuel and microbial sources. The aim of this study is to constrain factors that ultimately control the concentration of this potent greenhouse gas on global, regional, and local levels. While predictions of atmospheric methane isotopologues have been modeled, we present direct measurements that point to a different atmospheric methane composition and to a microbial flux with less clumping (greater deficits relative to stochastic) in both 13CH3D and 12CH2D2 than had been previously assigned. These differences make atmospheric isotopologue data sufficiently sensitive to variations in microbial to fossil fuel fluxes to distinguish between emissions scenarios such as those generated by different versions of EDGAR (the Emissions Database for Global Atmospheric Research), even when existing constraints on the atmospheric CH4 concentration profile as well as traditional isotopes are kept constant.

Capacity factors for electrical power generation from renewable and nonrenewable sources.

Given the dire consequences of climate change and the war in Ukraine, decarbonization of electrical power systems around the world must be accomplished, while avoiding recurring blackouts. A good understanding of performance and reliability of different power sources underpins this endeavor. As an energy transition involves different societal sectors, we must adopt a simple and efficient way of communicating the transition's key indicators. Capacity factor (CF) is a direct measure of the efficacy of a power generation system and of the costs of power produced. Since the year 2000, the explosive expansion of solar PV and wind power made their CFs more reliable. Knowing the long-time average CFs of different electricity sources allows one to calculate directly the nominal capacity required to replace the current fossil fuel mix for electricity generation or expansion to meet future demand. CFs are straightforwardly calculated, but they are rooted in real performance, not in modeling or wishful thinking. Based on the current average CFs, replacing 1 W of fossil electricity generation capacity requires installation of 4 W solar PV or 2 W of wind power. An expansion of the current energy mix requires installing 8.8 W of solar PV or 4.3 W of wind power.

Defining the challenges of Li extraction with olivine host: The roles of competitor and spectator ions.

The lithium supply issue mainly lies in the inability of current mining methods to access lithium sources of dilute concentrations and complex chemistry. Electrochemical intercalation has emerged as a highly selective method for lithium extraction; however, limited source compositions have been studied, which is insufficient to predict its applicability to the wide range of unconventional water sources (UWS). This work addresses the feasibility and identifies the challenges of Li extraction by electrochemical intercalation from UWS, by answering three questions: 1) Is there enough Li in UWS? 2) How would the solution compositions affect the competition of Li+ to major ions (Na+/Mg2+/K+/Ca2+)? 3) Does the complex solution composition affect the electrode stability? Using one-dimensional olivine FePO4 as the model electrode, we show the complicated roles of major ions. Na+ acts as the competitor ion for host storage sites. The competition from Na+ grants Mg2+ and Ca2+ being only the spectator ions. However, Mg2+ and Ca2+ can significantly affect the charge transfer of Li+ and Na+, therefore affecting the Li selectivity. We point to improving the selectivity of Li+ to Na+ as the key challenge for broadening the minable UWS using the olivine host.

Polarized and Bright Telecom C-Band Single-Photon Source from InP-Based Quantum Dots Coupled to Elliptical Bragg Gratings.

Bright, polarized, and high-purity single-photon sources in telecom wavelengths are crucial components in long-distance quantum communication, optical quantum computation, and quantum networks. Semiconductor InAs/InP quantum dots (QDs) combined with photonic cavities provide a competitive path, leading to optimal single-photon sources in this range. Here, we demonstrate a bright and polarized single-photon source operating in the telecom C-band based on an elliptical Bragg grating (EBG) cavity. With a significant Purcell enhancement of 5.25 ± 0.05, the device achieves a polarization ratio of 0.986, a single-photon purity of g2(0) = 0.078 ± 0.016, and a single-polarized photon collection efficiency of ∼24% at the first lens (NA = 0.65) without blinking. These findings suggest that C-band QD-based single-photon sources are potential candidates for advancing quantum communication.

A Gold Needle Tip Array Ultrafast Electron Source with High Beam Quality.

Electron sources are crucial elements in diverse applications such as electron microscopes, synchrotrons, and free-electron lasers. Nanometer-sharp needle tips are electron emitters with the highest beam quality, yet for a single needle the current is limited. Combining the emission of multiple needles promises large current yields while preserving the individual emitters' favorable properties. We present an ultrafast electron source consisting of a lithographically fabricated array of sharp gold tips illuminated with 25 fs laser pulses. The source provides up to 2000 electrons per pulse for moderate laser peak intensities of 1011 W/cm2 and a narrow energy width of 0.5 ± 0.05 eV at low current. The electron beam has a well-behaved Gaussian profile and is highly collimated, yielding a small normalized emittance on the order of nm·rad. These properties are well suited for applications requiring both high current and spatial resolution, such as free-electron light sources and chip-based particle accelerators.

Control of Quantized Spontaneous Emission from Single GaAs Quantum Dots Embedded in Huygens' Metasurfaces.

Advancements in photonic quantum information systems (QIS) have driven the development of high-brightness, on-demand, and indistinguishable semiconductor epitaxial quantum dots (QDs) as single photon sources. Strain-free, monodisperse, and spatially sparse local-droplet-etched (LDE) QDs have recently been demonstrated as a superior alternative to traditional Stranski-Krastanov QDs. However, integration of LDE QDs into nanophotonic architectures with the ability to scale to many interacting QDs is yet to be demonstrated. We present a potential solution by embedding isolated LDE GaAs QDs within an Al0.4Ga0.6As Huygens' metasurface with spectrally overlapping fundamental electric and magnetic dipolar resonances. We demonstrate for the first time a position- and size-independent, 1 order of magnitude increase in the collection efficiency and emission lifetime control for single-photon emission from LDE QDs embedded within the Huygens' metasurfaces. Our results represent a significant step toward leveraging the advantages of LDE QDs within nanophotonic architectures to meet the scalability demands of photonic QIS.

Engaging Communities in Emerging Infectious Disease Mitigation to Improve Public Health and Safety.

The COVID-19 pandemic highlighted the need for potent community-based tools to improve preparedness. We developed a community health-safety climate (HSC) measure to assess readiness to adopt health behaviors during a pandemic. We conducted a mixed-methods study incorporating qualitative methods (e.g., focus groups) to generate items for the measure and quantitative data from a February 2021 national survey to test reliability, multilevel construct, and predictive and nomologic validities. The 20-item HSC measure is unidimensional (Cronbach α = 0.87). All communities had strong health-safety climates but with significant differences between communities (F = 10.65; p<0.001), and HSC levels predicted readiness to adopt health-safety behaviors. HSC strength moderated relationships between HSC level and behavioral indicators; higher climate homogeneity demonstrated stronger correlations. The HSC measure can predict community readiness to adopt health-safety behaviors in communities to inform interventions before diseases spread, providing a valuable tool for public health authorities and policymakers during a pandemic.