Role of Hydrogen Bonding in Green Fluorescent Protein-like Chromophore Emission.

The fluorescence emission from green fluorescent protein (GFP) is known to be heavily influenced by hydrogen bonding between the core fluorophore and the surrounding side chains or water molecules. Yet how to utilize this feature for modulating the fluorescence of GFP chromophore or GFP-like fluorophore still remains elusive. Here we present theoretical calculations to predict how hydrogen bonding could influence the excited states of the GFP-like fluorophores. These studies provide both a new perspective for understanding the photophysical properties of GFP as well as a solid basis for the rational design of GFP-based fluorophores

Coupled analytical solutions for circular tunnels considering rock creep effects and time-dependent anchoring forces in prestressed bolts

Acknowledgement This work is supported by the National Science Foundation of China (51979281; 52034010), and the Natural Science Foundation of Shandong Province China (ZR2018MEE050).Peer reviewedPostprintPostprin

Data-driven approach for modeling Reynolds stress tensor with invariance preservation

The present study represents a data-driven turbulent model with Galilean invariance preservation based on machine learning algorithm. The fully connected neural network (FCNN) and tensor basis neural network (TBNN) [Ling et al. (2016)] are established. The models are trained based on five kinds of flow cases with Reynolds Averaged Navier-Stokes (RANS) and high-fidelity data. The mappings between two invariant sets, mean strain rate tensor and mean rotation rate tensor as well as additional consideration of invariants of turbulent kinetic energy gradients, and the Reynolds stress anisotropy tensor are trained. The prediction of the Reynolds stress anisotropy tensor is treated as user's defined RANS turbulent model with a modified turbulent kinetic energy transport equation. The results show that both FCNN and TBNN models can provide more accurate predictions of the anisotropy tensor and turbulent state in square duct flow and periodic flow cases compared to the RANS model. The machine learning based turbulent model with turbulent kinetic energy gradient related invariants can improve the prediction precision compared with only mean strain rate tensor and mean rotation rate tensor based models. The TBNN model is able to predict a better flow velocity profile compared with FCNN model due to a prior physical knowledge.Comment: 23 page

Mass-Related Traumatic Tissue Displacement and Behavior: A Screen for Treatments that Reduces Harm to Bystander Cells and Recovery of Function

In this study, we focused on a preclinical model of brain compression injury that has relevance to pathological conditions such as tumor, hematoma, blood clot, and intracerebral bony fragment. We investigated behavioral impairment as a result of rapid-onset small mass, and the factors involved in lesion formation and neuroplasticity. An epidural bead implantation method was adopted. Two sizes (1.5 mm and 2.0 mm thick) of hemisphere-shaped beads were used. The beads were implanted into various locations over the sensorimotor cortex (SMC—anterior, middle and posterior). The effects of early versus delayed bead removal were examined to model clinical neurosurgical or other treatment procedures. Forelimb and hind-limb behavioral deficits and recovery were observed, and histological changes were quantified to determine brain reaction to focal compression. Our results showed that the behavioral deficits of compression were influenced by the location, timing of compression release, and magnitude of compression. Even persistent compression by the thicker bead (2.0 mm) caused only minor behavioral deficits, followed by fast recovery within a week in most animals, suggesting a mild lesion pattern for this model. Brain tissue was compressed into a deformed shape under pressure with slight tissue damage, evidenced by pathological evaluation on hematoxylin and eosin (H&E)– and TUNEL–stained sections. Detectable but not severe behavioral dysfunction exhibited by this model makes it particularly suitable for direct assessment of adverse effects of interventions on neuroplasticity after brain compression injury. This model may permit development of treatment strategies to alleviate brain mass effects, without disrupting neuroplasticity.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63138/1/neu.2006.23.721.pd

Numerical analysis of yield properties of closed-cell aluminum foam under multiaxial loads by 3D voronoi model

Metallic foam is a typical porous material whose yield surface is related to not only von Mises equivalent stress but also the hydrostatic pressure. It is essential to study the yield properties of closed-cell aluminum foam under complex loading conditions. However, because the current experimental technique may support only a few proportions of multiaxial loading, it is hard to learn the yield surface well especially for the tensile hydrostatic pressure. In this article, we explored a numerical method to learn the yield properties of aluminum foam, in which the meso structures of aluminum foam were simulated by 3D Voronoi method. In addition, the yield surface of aluminum foam was drawn successfully with the numerical method. The main works included: (1) In our numerical simulation, we tested the calculating parameters such as mass scaling, elements number, and loading velocity on simulation results and verified the homogeneity of the 3D Voronoi model firstly. Furthermore, the optimized calculating parameters were determined by considering both reliability and feasibility of the calculation. Homogeneity of 3D Voronoi model was checked by the compression behaviors of aluminum in different directions. (2) In order to overcome the difficulty to determine critical yield state of metallic foams under complex loads, we recommended criterion by setting a dimensionless plastic dissipation value to determine the onset yield state of the foam under multiaxial loads. The critical value of dimensionless plastic dissipation was deduced from the common criterion—0.2% plastic strain in uniaxial loading, and the effect of relative densities on critical values was analyzed. (3) Three normal stresses were applied on cubic aluminum foam proportionally to implement the proportional loading. The different loading proportional factors of the three normal stresses were set widely to insure the yield surface including enough data, such as the hydrostatic loads cover from minimum negative to maximum positive values; each proportion has three loading proportional factors. Further, effects of the relative density on yield surface were investigated

The association between procalcitonin and acute kidney injury in patients stung by wasps

Introduction: The aim of this study was to investigate the status of serum procalcitonin (PCT) in patients stung by wasps and evaluate the association between PCT levels and acute kidney injury (AKI).Methods: Patients stung by wasps admitted to two tertiary hospitals between January 2017 and December 2020 were screened for enrollment. We evaluated serum PCT levels on admission in patients stung by wasps. The patients were divided into an AKI group and a non-AKI group. A logistic regression model was used to analyze the association between PCT status and AKI. The performance of PCT concentrations in predicting the occurrence of AKI was evaluated by the area under the receiver operating characteristic curve (AUROC).Results: A total of 138 patients were enrolled, and 66 patients suffered AKI. PCT levels were elevated in 78.99% of patients stung by wasps. Nearly half of the patients (47.83%) developed AKI. PCT levels were correlated with creatinine levels on admission (r = 0.787, 95% CI: 0.713–0.844). PCT levels in patients with AKI were higher than those in patients without AKI (p < 0.001). After adjustment for covariates, PCT levels on admission were independently associated with AKI (OR: 1.575, 95% CI: 1.071–2.317, p = 0.021). The AUROC of PCT levels on admission was 0.837 (95% CI, 0.771–0.902, p < 0.001). A PCT level of 0.57 μg/L was the cutoff for maximizing the Youden index; the specificity was 79.45%, and the sensitivity was 73.43%.Conclusion: Serum PCT levels may be a potential biomarker of AKI in patients stung by wasps

CO (J = 1–0) Observations toward Filamentary Molecular Clouds in the Galactic Region with l = [169.°75, 174.°75], b = [−0.°75, 0.°5]

We present observations of the CO isotopologues (12CO, 13CO, and C18O) toward the Galactic region with 169fdg75 ≤ l ≤ 174fdg75 and −0fdg75 ≤ b ≤ 0fdg5 using the Purple Mountain Observatory 13.7 m millimeter-wavelength telescope. Based on the 13CO (J = 1 − 0) data, we find five molecular clouds within the velocity range between −25 and 8 km s−1 that are all characterized by conspicuous filamentary structures. We have identified eight filaments with a length of 6.38–28.45 pc, a mean H2 column density of 0.70 × 1021–6.53 × 1021 cm−2, and a line mass of 20.24–161.91 M ☉ pc−1, assuming a distance of ~1.7 kpc. Gaussian fittings to the inner parts of the radial density profiles lead to a mean FWHM width of 1.13 ± 0.01 pc. The velocity structures of most filaments present continuous distributions with slight velocity gradients. We find that turbulence is the dominant internal pressure to support the fragmentation of filaments instead of thermal pressure. Most filaments have virial parameters smaller than 2; thus, they are gravitationally bound. Four filaments have an LTE line mass close to the virial line mass. We further extract dense clumps using the 13CO data and find that 64% of the clumps are associated with the filaments. According to the complementary IR data, most filaments have associated Class II young stellar objects. Class I objects are mainly found to be located in the filaments with a virial parameter close to 1. Within two virialized filaments, 12CO outflows have been detected, indicating ongoing star-forming activity therein.National Key Research & Development of China [2017YFA0402702]; European Unions Horizon 2020 research and innovation program [639459]; NSFC [11473069, 11503086, 11629302]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]