Calibration and alignment of metrology system for the Nuclear Spectroscopic Telescope Array mission

A metrology system to measure the on-orbit movement of a ten meter mast has been built for the Nuclear Spectroscopic Telescope Array (NuSTAR) x-ray observatory. In this paper, the metrology system is described, and the performance is measured. The laser beam stability is discussed in detail. Pre-launch alignment and calibration are also described. The invisible infrared laser beams must be aligned to their corresponding detectors without deploying the telescope in Earth’s gravity. Finally, a possible method for in-flight calibration of the metrology system is described

Microspinning: Local Surface Mixing via Rotation of Magnetic Microparticles for Efficient Small-Volume Bioassays

The need for high-throughput screening has led to the miniaturization of the reaction volume of the chamber in bioassays. As the reactor gets smaller, surface tension dominates the gravitational or inertial force, and mixing efficiency decreases in small-scale reactions. Because passive mixing by simple diffusion in tens of microliter-scale volumes takes a long time, active mixing is needed. Here, we report an efficient micromixing method using magnetically rotating microparticles with patterned magnetization induced by magnetic nanoparticle chains. Because the microparticles have magnetization patterning due to fabrication with magnetic nanoparticle chains, the microparticles can rotate along the external rotating magnetic field, causing micromixing. We validated the reaction efficiency by comparing this micromixing method with other mixing methods such as simple diffusion and the use of a rocking shaker at various working volumes. This method has the potential to be widely utilized in suspension assay technology as an efficient mixing strategy

Adenylyl cyclase-5 in the dorsal striatum function as a molecular switch for the generation of behavioral preferences for cue-directed food choices

BACKGROUND: Behavioral choices in habits and innate behaviors occur automatically in the absence of conscious selection. These behaviors are not easily modified by learning. Similar types of behaviors also occur in various mental illnesses including drug addiction, obsessive-compulsive disorder, schizophrenia, and autism. However, underlying mechanisms are not clearly understood. In the present study, we investigated the molecular mechanisms regulating unconditioned preferred behaviors in food-choices. RESULTS: Mice lacking adenylyl cyclase-5 (AC5 KO mice), which is preferentially expressed in the dorsal striatum, consumed food pellets nearly one after another in cages. AC5 KO mice showed aversive behaviors to bitter tasting quinine, but they compulsively chose quinine-containing AC5 KO-pellets over fresh pellets. The unusual food-choice behaviors in AC5 KO mice were due to the gain of behavioral preferences for food pellets containing an olfactory cue, which wild-type mice normally ignored. Such food-choice behaviors in AC5 KO mice disappeared when whiskers were trimmed. Conversely, whisker trimming in wildtype mice induced behavioral preferences for AC5 KO food pellets, indicating that preferred food-choices were not learned through prior experience. Both AC5 KO mice and wildtype mice with trimmed whiskers had increased glutamatergic input from the barrel cortex into the dorsal striatum, resulting in an increase in the mGluR1-dependent signaling cascade. The siRNA-mediated inhibition of mGluR1 in the dorsal striatum in AC5 KO mice and wildtype mice with trimmed whiskers abolished preferred choices for AC5 KO food pellets, whereas siRNA-mediated inhibition of mGluR3 glutamate receptors in the dorsal striatum in wildtype mice induced behavioral preferences for AC5 KO food pellets, thus mimicking AC5 KO phenotypes. CONCLUSIONS: Our results show that the gain and loss of behavioral preferences for a specific cue-directed option were regulated by specific cellular factors in the dorsal striatum, such that the preferred food choices were switched on when either the mGluR3-AC5 pathway was inactive or the mGluR1 pathway was active, whereas the preferred food-choices were switched off when mGluR1 or its downstream pathway was suppressed. These results identify the AC5 and mGluR system in the dorsal striatum as molecular on/off switches to direct decisions on behavioral preferences for cue-oriented options

Periodic Chiral Structures

The electromagnetic properties of a structure that is both chiral and periodic are investigated using coupled-mode equations. The chirality is characterized by the constitutive relations D = εE + iXicB and H = iXicE+B/µ, where Xic is the chiral admittance. The periodicity is described by a sinusoidal perturbation of the permittivity, permeability and chiral admittance. The coupled-mode equations are derived from physical considerations. The coupled-mode equations are used to examine bandgap structure and reflected and transmitted fields. Chirality is observed predominantly in transmission while periodicity is present in both reflection and transmission

One-Step Generation of a Drug-Releasing Hydrogel Microarray-On-A-Chip for Large-Scale Sequential Drug Combination Screening

Large-scale screening of sequential drug combinations, wherein the dynamic rewiring of intracellular pathways leads to promising therapeutic effects and improvements in quality of life, is essential for personalized medicine to ensure realistic cost and time requirements and less sample consumption. However, the large-scale screening requires expensive and complicated liquid handling systems for automation and therefore lowers the accessibility to clinicians or biologists, limiting the full potential of sequential drug combinations in clinical applications and academic investigations. Here, a miniaturized platform for high-throughput combinatorial drug screening that is "pipetting-free" and scalable for the screening of sequential drug combinations is presented. The platform uses parallel and bottom-up formation of a heterogeneous drug-releasing hydrogel microarray by self-assembly of drug-laden hydrogel microparticles. This approach eliminates the need for liquid handling systems and time-consuming operation in high-throughput large-scale screening. In addition, the serial replacement of the drug-releasing microarray-on-a-chip facilitates different drug exchange in each and every microwell in a simple and highly parallel manner, supporting scalable implementation of multistep combinatorial screening. The proposed strategy can be applied to various forms of combinatorial drug screening with limited amounts of samples and resources, which will broaden the use of the large-scale screening for precision medicine

Feature importance measures from random forest regressor using near-infrared spectra for predicting carbonization characteristics of kraft lignin-derived hydrochar

This study investigated the feature importance of near-infrared spectra from random forest regression models constructed to predict the carbonization characteristics of hydrochars produced by hydrothermal carbonization of kraft lignin. The model achieved high coefficients of determination of 0.989, 0.988, and 0.985 with root mean square errors of 0.254, 0.003, and 0.008 when predicting the carbon content, atomic O/C ratio, and H/C ratio, respectively. The random forest models outperformed the multilayer perceptron models for all predictions. In the feature importance analysis, the spectral regions at 1600–1800 nm, the first overtone of C–H stretching vibrations, and 2000–2300 nm, the combination bands, were highly important for predicting the carbon content and O/C predictions, whereas the region at 1250–1711 nm contributed to predicting H/C. The random forest models trained with the high-importance regions achieved better prediction performances than those trained with the entire spectral range, demonstrating the usefulness of the feature importance yielded by the random forest and the feasibility of selective application of the spectral data.This study was supported by the Korea Forestry Promotion Institute through the R&D Program for Forest Science Technology funded by the Korea Forest Service (Project No. 2020215D10-2122-AC01)

Development and Validation of 3-Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models.

Background Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF-related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. Methods and Results A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3-year new-onset AF prediction (C-statistic, 0.796 [95% CI, 0.785-0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C-statistic, 0.777 [95% CI, 0.766-0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. Conclusions Although the 3-year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF