A Novel Alcohol-Sensitive Site in the M3 Domain of the NMDA Receptor GluN2A Subunit

Accumulating studies have demonstrated that the N-methyl-D-aspartate receptor is one of the most important targets of ethanol in the central nervous system. Previous studies from this laboratory have found that one position in the third (F637) and two positions in the fourth (M823 and A825) membrane-associated (M) domains of the N-methyl-D-aspartate receptor GluN2A subunit modulate alcohol action and ion channel gating. Using site-directed mutagenesis and whole-cell patch-clamp recording, we have found an additional position in M3 of the GluN2A subunit, F636, which significantly influences ethanol sensitivity and functionally interacts with F637. Tryptophan substitution at F636 significantly decreased the ethanol IC50, decreased both peak and steady-state glutamate EC50, and altered agonist deactivation and apparent desensitization. There was a significant correlation between steadystate: peak current ratio, a measure of desensitization, and ethanol IC50 values for a series of mutants at this site, raising the possibility that changes in ethanol sensitivity may be secondary to changes in desensitization. Mutant cycle analysis revealed a significant interaction between F636 and F637 in regulating ethanol sensitivity. Our results suggest that F636 in the M3 domain of the GluN2A subunit not only influences channel gating and agonist potency, but also plays an important role in mediating the action of ethanol. These studies were supported by grants R01 AA015203-01A1 and AA015203-06A1 from the NIAAA to R.W.P

Extended Optical Model Analyses of Elastic Scattering and Fusion Cross Section Data for the 12^{12}C+208^{208}Pb System at Near-Coulomb-Barrier Energies by using a Folding Potential

Simultaneous $\chi^{2}$ analyses are performed for elastic scattering and fusion cross section data for the $^{12}$C+$^{208}$Pb system at near-Coulomb-barrier energies by using the extended optical model approach in which the polarization potential is decomposed into direct reaction (DR) and fusion parts. Use is made of the double folding potential as a bare potential. It is found that the experimental elastic scattering and fusion data are well reproduced without introducing any normalization factor for the double folding potential and also that both DR and fusion parts of the polarization potential determined from the $\chi^{2}$ analyses satisfy separately the dispersion relation. Furthermore, it is shown that the imaginary parts of both DR and fusion potentials at the strong absorption radius change very rapidly, which results in a typical threshold anomaly in the total imaginary potential as observed with tightly bound projectiles such as $\alpha$-particle and $^{16}$O.Comment: 26 pages, 7 figures, submitted to Physical Review

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science