Quantitative Mapping of Unmodulated Temperature Fields with Nanometer Resolution
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CRISPR gene activation (CRISPRa) tools have shown great promise for bacterial strain engineering but often require customization for each intended application. Our goal is to create generalizable CRISPRa tools that can overcome previous limitations of gene activation in bacteria. In eukaryotic cells, multiple activators can be combined for synergistic gene activation. To identify potential effectors for synergistic activation in bacteria, we systematically characterized bacterial activator proteins with a set of engineered synthetic promoters. We found that optimal target sites for different activators could vary by up to 200 bases in the region upstream of the transcription start site (TSS). These optimal target sites qualitatively matched previous reports for each activator, but the precise targeting rules varied between different promoters. By characterizing targeting rules in the same promoter context, we were able to test activator combinations with each effector positioned at its optimal target site. We did not find any activator combinations that produced synergistic activation, and we found that many combinations were antagonistic. Furthermore, this systematic investigation highlights fundamental mechanistic differences between bacterial and eukaryotic transcriptional activation systems and suggests that alternative strategies will be necessary for strong bacterial gene activation at arbitrary endogenous targets.
We present here detailed mechanistic studies of electrocatalytic hydrogenation (ECH) in aqueous solution over skeletal nickel cathodes to probe the various paths of reductive catalytic C–O bond cleavage among functionalized aryl ethers relevant to energy science. Heterogeneous catalytic hydrogenolysis of aryl ethers is important both in hydrodeoxygenation of fossil fuels and in upgrading of lignin from biomass. The presence or absence of simple functionalities such as carbonyl, hydroxyl, methyl, or methoxyl groups is known to cause dramatic shifts in reactivity and cleavage selectivity between sp3 C–O and sp2 C–O bonds. Specifically, reported hydrogenolysis studies with Ni and other catalysts have hinted at different cleavage mechanisms for the C–O ether bonds in α-keto and α-hydroxy β-O-4 type aryl ether linkages of lignin. Our new rate, selectivity, and isotopic labeling results from ECH reactions confirm that these aryl ethers undergo C–O cleavage via distinct paths. For the simple 2-phenoxy-1-phenylethane or its alcohol congener, 2-phenoxy-1-phenylethanol, the benzylic site is activated via Ni C–H insertion, followed by beta elimination of the phenoxide leaving group. But in the case of the ketone, 2-phenoxyacetophenone, the polarized carbonyl π system apparently binds directly with the electron rich Ni cathode surface without breaking the aromaticity of the neighboring phenyl ring, leading to rapid cleavage. Substituent steric and electronic perturbations across a broad range of β-O-4 type ethers create a hierarchy of cleavage rates that supports these mechanistic ideas while offering guidance to allow rational design of the catalytic method. Furthermore, on the basis of the new insights, the usage of cosolvent acetone is shown to enable control of product selectivity.
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Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with potential applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes such as (GT)n-SWCNTs recognizing the neuromodulator, dopamine. It remains unclear how the ssDNA conformation on the SWCNT surface contributes to functionality, as observations have been limited to computational models or experiments under dehydrated conditions that differ substantially from the aqueous biological environments in which the nanosensors are applied. We demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two (GT) n ssDNA oligomer lengths (n = 6, 15) that are used on SWCNTs in the context of dopamine sensing and measure the ssDNA conformational changes as a function of ionic strength and during dopamine interaction. Here, we show that the shorter oligomer, (GT) 6 , adopts a more periodically ordered ring structure along the SWCNT axis (inter-ssDNA distance of 8.6 ± 0.3 nm), compared to the longer (GT) 15 oligomer (most probable 5'-to-5' distance of 14.3 ± 1.1 nm). During molecular recognition, XSI reveals that dopamine elicits simultaneous axial elongation and radial constriction of adsorbed ssDNA on the SWCNT surface. Our approach using XSI to probe solution-phase morphologies of polymer-functionalized SWCNTs can be applied to yield insights into sensing mechanisms and inform future design strategies for nanoparticle-based sensors.
The sizes of the basins of attraction on the potential energy surface are helpful indicators in determining the experimental synthesizability of metastable phases. In principle, these basins can be controlled with changes in thermodynamic conditions such as composition, pressure, and surface energy. Herein, we use random structure sampling to computationally study how alloying smoothly perturbs basin of attraction sizes. The TaC 1-x N x pseudobinary is an ideal test system given the structural and polymorphic contrast of its parent compounds and their technological relevance as epitaxial substrates for Al 1-x Ga x N. While we find limited thermodynamic stability across all computationally observed phases, random structure sampling shows a significant composition region where the rocksalt basin dominates. As such, we predict the potential for the nonequilibrium synthesis of metastable rocksalt TaC 1-x N x alloys as substrates for Al 1-x Ga x N. At higher nitrogen concentrations, other low-energy metastable polymorphs emerge that continue to retain the hexagonal close packing suitable for III-N growth. Confidence in these trends was established through uncertainty quantification of the basin sizes and energy distributions; such analysis utilized the Beta and Dirichlet distributions. In conclusion, we also find (a) polymorph basin sizes can be rationalized in terms of energetic preferences for different coordination environments; and (b) basin sizes universally shrink with increasing nitrogen content, making the system more prone to amorphous growth.
An important subclass of metal–organic frameworks (MOFs) exhibits cooperative flexibility, wherein individual crystallites undergo global structural phase changes in response to external stimuli. Where cooperative flexibility results in reversible changes between crystalline states of distinct accessible porosity, these frameworks can exhibit rare yet desirable behaviors that cannot be explained by local dynamics alone. Yet, the chemical and structural origins of cooperative flexibility and how frameworks undergo these reversible phase changes at the atomic level remain poorly understood. Deliberate design for specific applications is therefore exceedingly difficult, and there is great impetus to develop a fundamental understanding of this phenomenon. Here, an effective and widely accessible computational approach is developed, which is designed to provide microscopic resolution via direct comparison to experimental data along the desorption-guided pathway. The strategy is applied to explain the desorption-induced phase change in an experimentally well-characterized framework, CdIF-13 (sod-Cd(benzimidazolate)2), where experiment alone was unable to resolve the atomistically detailed phase change landscape. Our findings reveal that the cooperative phase change pathways are adsorbate dependent with thermodynamics of intermediate structural states dictated by a nuanced interplay of ligand orientation, skeletal symmetry, and modes of surface adsorption. The results reveal that this isotropically flexible framework is “chaperoned” through a complex energy landscape by specific adsorbates, revealed by the reported computational approach with atomic-level insight and validated by experimentally determined structures. Thus, this work facilitates both understanding and future design of flexible materials for applications in gas storage, transport, delivery, and separation technologies.
We measured the horizontal winds in Jupiter's Great Red Spot (GRS) using data from the WFC3/UVIS instrument on board the Hubble Space Telescope (HST). The data cover 11 epochs from 2009 to 2020. Long-term monotonic trends in size and shape previously noted from the visible cloud appearance are paralleled by changes in the high-speed ring around the vortex. The circularization of the GRS cannot be explained by changes in the horizontal wind shear of the surrounding environment. The velocity fields suggest no long-term trend in the static stability inside or outside the vortex. Instead, the changes are accompanied by a 4%–8% increase in the mean wind speeds of the high-speed ring from 2009 to 2020. Changes in the wind field coincided with the South Equatorial Belt Outbreak storms of 2016–2017, but not with 2019 "flaking" events involving detachment of red material from the main oval.
During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on-ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.
Subsurface processes significantly influence surface dynamics in permafrost regions, necessitating utilizing diverse geophysical methods to reliably constrain permafrost characteristics. This research uses multiple geophysical techniques to explore the spatial variability of permafrost in undisturbed tundra and its degradation in disturbed tundra in Utqiagvik, Alaska. Here, we integrate multiple quantitative techniques, including multichannel analysis of surface waves (MASW), electrical resistivity tomography (ERT), and ground temperature sensing, to study heterogeneity in permafrost’s geophysical characteristics. MASW results reveal active layer shear wave velocities (V s ) between 240 and 370 m/s, and permafrost V s between 450 and 1,700 m/s, typically showing a low-high-low velocity pattern. Additionally, we find an inverse relationship between in situ V s and ground temperature measurements. The V s profiles along with electrical resistivity profiles reveal cryostructures such as cryopeg and ice-rich zones in the permafrost layer. The integrated results of MASW and ERT provide valuable information for characterizing permafrost heterogeneity and cryostructure. Corroboration of these geophysical observations with permafrost core samples’ stratigraphies and salinity measurements further validates these findings. This combination of geophysical and temperature sensing methods along with permafrost core sampling confirms a robust approach for assessing permafrost’s spatial variability in coastal environments. Our results also indicate that civil infrastructure systems such as gravel roads and pile foundations affect permafrost by thickening the active layer, lowering the V s , and reducing heterogeneity. We show how the resulting V s profiles can be used to estimate key parameters for designing buildings in permafrost regions and maintaining existing infrastructure in polar regions.
Low-lying coastal urban cities face significant flooding risks from river flooding (fluvial), storm surges and high tides (coastal), and intense local rainfall (pluvial). Accurately assessing these hazards requires modeling frameworks capable of capturing both the individual and combined effects of multiple flood drivers, as well as the diversity of flood scenarios that can arise from their interactions. In this study, we implemented a physics-based, high-resolution modeling approach to assess flood hazards in Philadelphia, PA, a coastal city exemplifying multi-driver flood risks over multiple decades (1985–2019), by simulating a wide range of flood events at a 10-m resolution. By integrating watershed, coastal, and urban flood models, we explicitly resolved the interaction between fluvial, pluvial, and coastal processes across the city. From these simulations, we identified flood hazard hotspots and systematically attributed the primary drivers of flooding for each event. The results suggested that 44% of Philadelphia's flood events were compound floods, primarily driven by fluvial-surge and fluvial-pluvial combinations. Notably, 77% of these events involved fluvial flooding, either as a single flood driver or in combination with other drivers, underscoring the dominant role of riverine processes in the city's flood hazard. Overall, this study demonstrates the value of a comprehensive, process-based approach for urban flood hazard assessment and highlights the importance of considering the full spectrum of flood scenarios to inform targeted and adaptive flood management strategies in coastal cities.
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