West Mountain, Moon, Venus, and Stars

Here we show that a solvent-exposed f-position (i.e., residue 14) within a well-characterized trimeric helix bundle can facilitate a stabilizing long-range synergistic interaction involving b-position Glu10 (i.e., i–4 relative to residue 14) and c-position Lys18 (i.e., i+4), depending the identity of residue 14. The extent of stabilization associated with the Glu10-Lys18 pair depends primarily on the presence of a side-chain hydrogen-bond donor at residue 14; the non-polar or hydrophobic character of residue 14 plays a smaller but still significant role. Crystal structures and molecular dynamics simulations indicate that Glu10 and Lys18 do not interact directly with each other but suggest the possibility that the proximity of residue 14 with Lys18 allows Glu10 to interact favorably with nearby Lys7. Subsequent thermodynamic experiments confirm the important role of Lys7 in the large synergistic stabilization associated with the Glu10-Lys18 pair. Our results highlight the exquisite complexity and surprising long range of synergistic interactions among b-, c-, and f-position residues within helix bundles, suggesting new possibilities for engineering hyper-stable helix bundles and emphasizing the need to consider carefully the impact of substitutions at these positions for application-specific purposes.

Because of the vital role of temperature in many biological processes studied in microfluidic devices, there is a need to develop improved temperature sensors and data analysis algorithms. The photoluminescence (PL) of nanocrystals (quantum dots) has been successfully used in microfluidic temperature devices, but the accuracy of the reconstructed temperature has been limited to about 1 K over a temperature range of tens of degrees. A machine learning algorithm consisting of a fully connected network of seven layers with decreasing numbers of nodes was developed and applied to a combination of normalized spectral and time-resolved PL data of CdTe quantum dot emission in a microfluidic device. The data used by the algorithm were collected over two temperature ranges: 10–300 K and 298–319 K. The accuracy of each neural network was assessed via a mean absolute error of a holdout set of data. For the low-temperature regime, the accuracy was 7.7 K, or 0.4 K when the holdout set is restricted to temperatures above 100 K. For the high-temperature regime, the accuracy was 0.1 K. This method provides demonstrates a potential machine learning approach to accurately sense temperature in microfluidic (and potentially nanofluidic) devices when the data analysis is based on normalized PL data when it is stable over time.

We present a variability study of the lowest-luminosity Seyfert 1 nucleus of the galaxy NGC 4395 based on photometric monitoring campaigns in 2017 and 2018. Using 22 ground-based and space telescopes, we monitored NGC 4395 with a ∼5-minute cadence during a period of 10 days and obtained light curves in the ultraviolet (UV), V, J, H, and K/Ks bands, as well as narrowband Hα. The rms variability is ∼0.13 mag in the Swift UVM2 and V filter light curves, decreasing down to ∼0.01 mag in the K filter. After correcting for the continuum contribution to the Hα narrow band, we measured the time lag of the Hα emission line with respect to the V-band continuum as – minutes in 2017 and – minutes in 2018, depending on assumptions about the continuum variability amplitude in the Hα narrow band. We obtained no reliable measurements for the continuum-to-continuum lag between UV and V bands and among near-IR bands, owing to the large flux uncertainty of UV observations and the limited time baseline. We determined the active galactic nucleus (AGN) monochromatic luminosity at 5100 Å, , after subtracting the contribution of the nuclear star cluster. While the optical luminosity of NGC 4395 is two orders of magnitude lower than that of other reverberation-mapped AGNs, NGC 4395 follows the size–luminosity relation, albeit with an offset of 0.48 dex (≥2.5σ) from the previous best-fit relation of Bentz et al.