Publications

A method for photopatterning multiple enzymes on porous polymer monoliths within microfluidic devices has been developed and used to perform spatially separated multienzymatic reactions. To reduce nonspecific adsorption of enzymes on the monolith, its pore surface was modified by grafting poly(ethylene glycol), followed by surface photoactivation and enzyme immobilization in the presence of a nonionic surfactant. Characterization of bound horseradish peroxidase (HRP) was carried out using a reaction in which the steady-state profiles of the fluorescent reaction product could be measured in situ and then analyzed using a plug-flow bioreactor model to determine the observed maximum reaction rate and Michaelis constant. The Michaelis constant of 1.9 micro mol/L agrees with previously published values. Mass-transfer limitations were evident at relatively low flow rates but were absent at higher flow rates. Sequential multienzymatic reactions were demonstrated using the patternwise assembly of two- and three-enzyme systems. Glucose oxidase (GOX) and HRP were patterned in separate regions of a single channel, and product formation was analyzed as a function of flow direction. Significant product formation occurred only in the GOX to HRP direction. A three-enzyme sequential reaction was performed using invertase, GOX, and HRP. All possible arrangements of the three enzymes were tested, but significant product formation was only observed when the enzymes were in the correct sequential order. Photopatterning enzymes on polymer monoliths provides a simple technique for preparing spatially localized multiple-enzyme microreactors capable of directional synthesis.

Mott insulating behavior is induced by strong electron correlation and can lead to exotic states of matter such as unconventional superconductivity and quantum spin liquids. Recent advances in van der Waals material synthesis enable the exploration of novel Mott systems in the two-dimensional limit. Here we report characterization of the local electronic properties of single- and few-layer 1T-TaSe2 via spatial- and momentum-resolved spectroscopy involving scanning tunneling microscopy and angle-resolved photoemission. Our combined experimental and theoretical study indicates that electron correlation induces a robust Mott insulator state in single-layer 1T-TaSe2 that is accompanied by novel orbital texture. Inclusion of interlayer coupling weakens the insulating phase in 1T-TaSe2, as seen by strong reduction of its energy gap and quenching of its correlation-driven orbital texture in bilayer and trilayer 1T-TaSe2. Our results establish single-layer 1T-TaSe2 as a useful new platform for investigating strong correlation physics in two dimensions.

Copper is an essential element for sustaining life. However, disruptions in copper homeostasis underpin disease, as illustrated by cuproptosis, an emerging form of cell death resulting from aberrant accumulation of copper pools in loosely bound, labile forms. Along these lines, activity-based sensing (ABS) offers a powerful strategy for tracking labile copper fluxes with metal and oxidation state selectivity by exploiting analyte reactivity for analyte detection. Traditional ABS probes for Cu(I), the major oxidation state of copper in cells, are selective but require O₂ as a coadditive, thus limiting their temporal resolution and sensitivity. Here, we present the design, synthesis, and biological evaluation of a first-generation ABS strategy for direct Cu(I) sensing by leveraging alkyne-directed cleavage reactivity. Copper Alkyne Probe-1 (CAP-1) features a rapid response to changes in intracellular Cu(I) pools with over 400-fold selectivity for Cu(I) over competing biological metals. We apply this probe to identify novel metal-metal crosstalk in cuproptosis, where we observe that Mn(II) exposure sensitizes cells to cuproptosis through upregulating the mitochondrial reductase FDX1 and depleting reduced glutathione, thus synergistically elevating labile Cu(I) levels. By revealing an interplay between copper and manganese in regulating cell death, this work provides a starting point for broader investigations of metal-metal nutrient crosstalk in biology and medicine.

Abstract not Available.

Trials of decision aids developed for use in shared decision making find that patients engaged in that process tend to choose more conservative treatment for preference-sensitive conditions. Shared decision making is a collaborative process in which clinicians and patients discuss trade-offs and benefits of specific treatment options in light of patients' values and preferences. Decision aids are paper, video, or web-based tools intended to help patients match personal preferences with available treatment options. We analyzed data for 2012-15 about patients within the ten High Value Healthcare Collaborative member systems who were exposed to condition-specific decision aids in the context of consultations for hip and knee osteoarthritis, with the intention that the aids be used to support shared decision making. Compared to matched patients not exposed to the decision aids, those exposed had two-and-a-half times the odds of undergoing hip replacement surgery and nearly twice the odds of undergoing knee replacement surgery within six months of the consultation. These findings suggest that health care systems adopting decision aids developed for use in shared decision making, and used in conjunction with hip and knee osteoarthritis consultations, should not expect reduced surgical utilization.

Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning, which is highly memory-intensive for tasks with high-resolution data, e.g., video understanding, small object detection, and point cloud analysis. In this paper, we propose Dynamic Reversible Dual-Residual Networks, or Dr$^2$Net, a novel family of network architectures that acts as a surrogate network to finetune a pretrained model with substantially reduced memory consumption. Dr$^2$Net contains two types of residual connections, one maintaining the residual structure in the pretrained models, and the other making the network reversible. Due to its reversibility, intermediate activations, which can be reconstructed from output, are cleared from memory during training. We use two coefficients on either type of residual connections respectively, and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision. We evaluate Dr$^2$Net on various pretrained models and various tasks, and show that it can reach comparable performance to conventional finetuning but with significantly less memory usage.

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Snf Sherman Barn. This is the FLUXNET version of the carbon flux data for the site US-Snf Sherman Barn produced by applying the standard ONEFlux (1F) software. Site Description - The Sherman Barn is pasture site on Sherman Island, that is managed by the California Department of Water Resources (DWR). A flux tower equipped to analyze energy, H2O, CO2, and CH4 fluxes was installed on June, 2018

The reaction pathways for the dehydrogenation of ethane, propane, and butane, over Pt are analyzed using density functional theory (DFT). Pt nanoparticles are represented by a tetrahedral Pt4 cluster. The objectives of this work were to establish which step is rate limiting and which one controls the selectivity for forming alkenes as opposed to causing further dehydrogenation of adsorbed alkenes to produce precursors responsible for catalyst deactivation due to coking. Further objectives of this work are to identify the role of adsorbed hydrogen, derived from H2 fed together with the alkane, on the reaction pathway, and the role of replacing one of the four Pt atoms by a Sn atom. A comparison of Gibbs free energies shows that in all cases the rate-determining step is cleavage of a C-H bond upon alkane adsorption. The selectivity to alkene formation versus precursors to coking is dictated by the relative magnitudes of the activation energies for alkene desorption and dehydrogenation of the adsorbed alkene. The presence of an adsorbed H atom on the cluster facilitates alkene desorption relative to dehydrogenation of the adsorbed alkene. Substitution of a Sn atom in the cluster to produce a Pt3Sn cluster leads to a downward shift of the potential energy surface for the reaction and causes an increase of the activity of the catalyst as suggested by recent experiments due to the lower net activation barrier for the rate limiting step. However, the introduction of Sn does not alter the relative activation barriers for gas-phase alkene formation versus loss of hydrogen from the adsorbed alkene, the process leading to the formation of coke precursors.