Aggregation-Prone Structural Ensembles of Transthyretin Collected With Regression Analysis for NMR Chemical Shift

Monomer dissociation and subsequent misfolding of the transthyretin (TTR) is one of the most critical causative factors of TTR amyloidosis. TTR amyloidosis causes several human diseases, such as senile systemic amyloidosis and familial amyloid cardiomyopathy/polyneuropathy; therefore, it is important to understand the molecular details of the structural deformation and aggregation mechanisms of TTR. However, such molecular characteristics are still elusive because of the complicated structural heterogeneity of TTR and its highly sensitive nature to various environmental factors. Several nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) studies of TTR variants have recently reported evidence of transient aggregation-prone structural states of TTR. According to these studies, the stability of the DAGH β-sheet, one of the two main β-sheets in TTR, is a crucial determinant of the TTR amyloidosis mechanism. In addition, its conformational perturbation and possible involvement of nearby structural motifs facilitates TTR aggregation. This study proposes aggregation-prone structural ensembles of TTR obtained by MD simulation with enhanced sampling and a multiple linear regression approach. This method provides plausible structural models that are composed of ensemble structures consistent with NMR chemical shift data. This study validated the ensemble models with experimental data obtained from circular dichroism (CD) spectroscopy and NMR order parameter analysis. In addition, our results suggest that the structural deformation of the DAGH β-sheet and the AB loop regions may correlate with the manifestation of the aggregation-prone conformational states of TTR. In summary, our method employing MD techniques to extend the structural ensembles from NMR experimental data analysis may provide new opportunities to investigate various transient yet important structural states of amyloidogenic proteins. Copyright © 2021 Yang, Kim, Muniyappan, Lee, Kim and Yu.1

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir

The latest trend in neuromuscular monitoring: return of the electromyography

To reduce the risk of residual neuromuscular blockade, neuromuscular monitoring must be performed. Acceleromyography (AMG)-based neuromuscular monitoring was regarded as “clinical gold standard” and widely applied. However, issues related to patient’s posture and overestimation of train-of-four ratio associated with AMG-based neuromuscular monitoring have increased. Recently, electromyography (EMG)-based neuromuscular monitoring is receiving renewed attention, since it overcomes AMG’s weaknesses. However, both AMG-based and EMG-based systems are useful when certain considerations are followed. Ultimately, to assure the patient’s good outcomes, the choice of monitoring system is not as important as the monitoring itself, which should be always implemented in such patients

Design and Fabrication of a 300 GHz Modified Sine Waveguide Traveling-Wave Tube Using a Nano Computer Numerical Control Machine

The sine waveguide slow-wave structure is a promising interaction circuit for traveling-wave tubes in the terahertz region because it possesses advantageous properties such as high transmission, easy fabrication, and elimination of the electron beam tunnel. These waveguides could be fabricated by nanocomputer numerical control (CNC) machining, a fabrication method capable of fabricating microscale components. In our study, we evaluate the practical feasibility of manufacturing 300-GHz sine waveguides with the ideal design, using nano-CNC machining. It is found that the ideal sine waveguide circuit must be modified, because of the limitations imposed by the actual tool size of the nano-CNC machine. Simulations of cold- and hot-tests of the circuit—including the electron beam effect—were conducted for both the ideal and the modified sine waveguide circuits. A modified sine waveguide was successfully machined using a nano-CNC machine with a 0.12-mm diameter tool tip. The S-parameters of the fabricated circuit were measured and compared to simulation data. A detailed analysis of the measured transmission loss was performed, and this loss was found to be attributable to a gap left by the assembly process between the two copper plates