Publications by authors named "E A Gross"

Nicotinamide Adenine Dinucleotide (NAD) is implicated in bioenergetics, DNA repair, and senescence. Depletion of NAD is associated with aging and neurodegenerative disease, prompting a growing interest in NAD supplementation. With rising over-the-counter use of NAD, understanding their impact on anesthetic recovery becomes essential.

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High-energy nuclear collisions create a quark-gluon plasma, whose initial condition and subsequent expansion vary from event to event, impacting the distribution of the eventwise average transverse momentum [P([p_{T}])]. Disentangling the contributions from fluctuations in the nuclear overlap size (geometrical component) and other sources at a fixed size (intrinsic component) remains a challenge. This problem is addressed by measuring the mean, variance, and skewness of P([p_{T}]) in ^{208}Pb+^{208}Pb and ^{129}Xe+^{129}Xe collisions at sqrt[s_{NN}]=5.

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Objective: The aim of this study was to evaluate the association of neighborhood-level and individual-level measures of socioeconomic status with readmission, complication rates, and postoperative length of stay of patients with cervical spondylotic myelopathy (CSM) in the Deep South.

Methods: The authors identified all patients undergoing surgical intervention for the treatment of CSM from November 2010 to February 2022 using Current Procedural Terminology and ICD-9/ICD-10 codes. Patient demographic, socioeconomic, perioperative, and postoperative data for each patient were collected via review of the electronic medical record.

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The observation of spin-dependent transmission of electrons through chiral molecules has led to the discovery of chiral-induced spin selectivity (CISS). The remarkably high efficiency of the spin polarizing effect has recently gained substantial interest due to the high potential for future sustainable hybrid chiral molecule magnetic applications. However, the fundamental mechanisms underlying the chiral-induced phenomena remain to be understood fully.

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Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop particle-flow neural-assisted simulations (parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles).

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