Search for: All records

Abstract Oxygen-containing complex organic molecules are key precursors to biorelevant compounds fundamental for the origins of life. However, the untangling of their interstellar formation mechanisms has just scratched the surface, especially for oxygen-containing cyclic molecules. Here, we present the first laboratory simulation experiments featuring the formation of all three C₂H₄O isomers—ethylene oxide (c–C₂H₄O), acetaldehyde (CH₃CHO), and vinyl alcohol (CH₂CHOH)—in low-temperature model interstellar ices composed of carbon monoxide (CO) and ethanol (C₂H₅OH). Ice mixtures were exposed to galactic cosmic-ray proxies with an irradiation dose equivalent to a cold molecular cloud aged (7 ± 2) × 10⁵yr. These biorelevant species were detected in the gas phase through isomer-selective photoionization reflectron time-of-flight mass spectrometry during temperature-programmed desorption. Isotopic labeling experiments reveal that ethylene oxide is produced from ethanol alone, providing the first experimental evidence to support the hypothesis that ethanol serves as a precursor to the prototype epoxide in interstellar ices. These findings reveal feasible pathways for the formation of all three C₂H₄O isomers in ethanol-rich interstellar ices, offering valuable constraints on astrochemical models for their formation. Our results suggest that ethanol is a critical precursor to C₂H₄O isomers in interstellar environments, representing a critical step toward unraveling the formation mechanisms of oxygen-containing cyclic molecules, aldehydes, and their enol tautomers from alcohols in interstellar ices. 

Urban heat mitigation is a pressing concern for cities. Intense urban heat poses a threat to human health and urban sustainability. Tree planting is one of the most widely employed nature-based heat mitigation methods worldwide. Therefore, city policy makers require knowledge of how much temperature will be reduced by increasing urban tree canopy (UTC). Cooling efficiency (CE), which was been proposed to quantify the magnitude of temperature reduction associated with a 1% increase in UTC, has been primarily investigated at smaller scales previously. However, such small-scale results cannot be used to develop policy at the whole-city scale. This study developed a method that reveals the scaling relations of CE so as to predict its effects at the city scale. CE was found to follow the form of a power law as spatial scale increased from the small analytical units through intermediate size units up to the extent of a whole city. The power law form appeared consistently across cities with different climate backgrounds during summer daylight hours. Furthermore, the power law form was robust within cities under different summer weather conditions. The power-law scaling approach can thus be used to predict CE at the whole-city scale, providing a useful tool for managers to set UTC goals to mitigate extreme urban heat. 

ABSTRACT Haemophilus ducreyicauses the genital ulcer disease chancroid and cutaneous ulcers in children. To study its pathogenesis, we developed a human challenge model in which we infect the skin on the upper arm of human volunteers withH. ducreyito the pustular stage of disease. The model has been used to define lesional architecture, describe the immune infiltrate into the infected sites using flow cytometry, and explore the molecular basis of the immune response using bulk RNA-seq. Here, we used single cell RNA-seq (scRNA-seq) and spatial transcriptomics to simultaneously characterize multiple cell types within infected human skin and determine the cellular origin of differentially expressed transcripts that we had previously identified by bulk RNA-seq. We obtained paired biopsies of pustules and wounded (mock infected) sites from five volunteers for scRNA-seq. We identified 13 major cell types, including T- and NK-like cells, macrophages, dendritic cells, as well as other cell types typically found in the skin. Immune cell types were enriched in pustules, and some subtypes within the major cell types were exclusive to pustules. Sufficient tissue specimens for spatial transcriptomics were available from four of the volunteers. T- and NK-like cells were highly associated with multiple antigen presentation cell types. In pustules, type I interferon stimulation was high in areas that were high in antigen presentation—especially in macrophages near the abscess—compared to wounds. Together, our data provide a high-resolution view of the cellular immune response to the infection of the skin with a human pathogen.IMPORTANCEA high-resolution view of the immune infiltrate due to infection with an extracellular bacterial pathogen in human skin has not yet been defined. Here, we used the human skin pathogenHaemophilus ducreyiin a human challenge model to identify on a single cell level the types of cells that are present in volunteers who fail to spontaneously clear infection and form pustules. We identified 13 major cell types. Immune cells and immune-activated stromal cells were enriched in pustules compared to wounded (mock infected) sites. Pustules formed despite the expression of multiple pro-inflammatory cytokines, such as IL-1β and type I interferon. Interferon stimulation was most evident in macrophages, which were proximal to the abscess. The pro-inflammatory response within the pustule may be tempered by regulatory T cells and cells that express indoleamine 2,3-dioxygenase, leading to failure of the immune system to clearH. ducreyi. 

Abstract Aldehydes are ubiquitous in star-forming regions and carbonaceous chondrites, serving as essential intermediates in metabolic pathways and molecular mass growth processes to vital biomolecules necessary for the origins of life. However, their interstellar formation mechanisms have remained largely elusive. Here, we unveil the formation of lactaldehyde (CH₃CH(OH)CHO) by barrierless recombination of formyl (HĊO) and 1-hydroxyethyl (CH₃ĊHOH) radicals in interstellar ice analogs composed of carbon monoxide (CO) and ethanol (CH₃CH₂OH). Lactaldehyde and its isomers 3-hydroxypropanal (HOCH₂CH₂CHO), ethyl formate (CH₃CH₂OCHO), and 1,3-propenediol (HOCH₂CHCHOH) are identified in the gas phase utilizing isomer-selective photoionization reflectron time-of-flight mass spectrometry and isotopic substitution studies. These findings reveal fundamental formation pathways for complex, biologically relevant aldehydes through non-equilibrium reactions in interstellar environments. Once synthesized, lactaldehyde can act as a key precursor to critical biomolecules such as sugars, sugar acids, and amino acids in deep space. 

Abstract Two-dimensional (2D) ferroelectric and magnetic van der Waals materials are emerging platforms for the discovery of novel cooperative quantum phenomena and development of energy-efficient logic and memory applications as well as neuromorphic and topological computing. This review presents a comprehensive survey of the rapidly growing 2D ferroic family from the synthesis perspective, including brief introductions to the top-down and bottom-up approaches for fabricating 2D ferroic flakes, thin films, and heterostructures as well as the important characterization techniques for assessing the sample properties. We also discuss the key challenges and future directions in the field, including scalable growth, property control, sample stability, and integration with other functional materials. 

Boolean matrix factorization (BMF) has been widely utilized in fields such as recommendation systems, graph learning, text mining, and -omics data analysis. Traditional BMF methods decompose a binary matrix into the Boolean product of two lower-rank Boolean matrices plus homoscedastic random errors. However, real-world binary data typically involves biases arising from heterogeneous row- and column-wise signal distributions. Such biases can lead to suboptimal fitting and unexplainable predictions if not accounted for. In this study, we reconceptualize the binary data generation as the Boolean sum of three components: a binary pattern matrix, a background bias matrix influenced by heterogeneous row or column distributions, and random flipping errors. We introduce a novel Disentangled Representation Learning for Binary matrices (DRLB) method, which employs a dual auto-encoder network to reveal the true patterns. DRLB can be seamlessly integrated with existing BMF techniques to facilitate bias-aware BMF. Our experiments with both synthetic and real-world datasets show that DRLB significantly enhances the precision of traditional BMF methods while offering high scalability. Moreover, the bias matrix detected by DRLB accurately reflects the inherent biases in synthetic data, and the patterns identified in the bias-corrected real-world data exhibit enhanced interpretability. 

Glyceric acid [HOCH₂CH(OH)COOH]—the simplest sugar acid—represents a key molecule in biochemical processes vital for metabolism in living organisms such as glycolysis. Although critically linked to the origins of life and identified in carbonaceous meteorites with abundances comparable to amino acids, the underlying mechanisms of its formation have remained elusive. Here, we report the very first abiotic synthesis of racemic glyceric acid via the barrierless radical-radical reaction of the hydroxycarbonyl radical (HOĊO) with 1,2-dihydroxyethyl (HOĊHCH₂OH) radical in low-temperature carbon dioxide (CO₂) and ethylene glycol (HOCH₂CH₂OH) ices. Using isomer-selective vacuum ultraviolet photoionization reflectron time-of-flight mass spectrometry, glyceric acid was identified in the gas phase based on the adiabatic ionization energies and isotopic substitution studies. This work reveals the key reaction pathways for glyceric acid synthesis through nonequilibrium reactions from ubiquitous precursor molecules, advancing our fundamental knowledge of the formation pathways of key biorelevant organics—sugar acids—in deep space. 

Boolean matrix factorization (BMF) has been widely utilized in fields such as recommendation systems, graph learning, text mining, and -omics data analysis. Traditional BMF methods decompose a binary matrix into the Boolean product of two lower-rank Boolean matrices plus homoscedastic random errors. However, real-world binary data typically involves biases arising from heterogeneous row- and column-wise signal distributions. Such biases can lead to suboptimal fitting and unexplainable predictions if not accounted for. In this study, we reconceptualize the binary data generation as the Boolean sum of three components: a binary pattern matrix, a background bias matrix influenced by heterogeneous row or column distributions, and random flipping errors. We introduce a novel Disentangled Representation Learning for Binary matrices (DRLB) method, which employs a dual auto-encoder network to reveal the true patterns. DRLB can be seamlessly integrated with existing BMF techniques to facilitate bias-aware BMF. Our experiments with both synthetic and real-world datasets show that DRLB significantly enhances the precision of traditional BMF methods while offering high scalability. Moreover, the bias matrix detected by DRLB accurately reflects the inherent biases in synthetic data, and the patterns identified in the bias-corrected real-world data exhibit enhanced interpretability.