Contributions of microRNAs to peripheral insulin sensitivity

An extensive literature base combined with advances in sequencing technologies demonstrated microRNA levels correlate with various metabolic diseases. Mechanistic studies also establish microRNAs regulate central metabolic pathways and thus play vital roles in maintaining organismal energy balance and metabolic homeostasis. This review highlights research progress on the roles and regulation of microRNAs in the peripheral tissues that confer insulin sensitivity. We discuss sequencing technologies used to comprehensively define the target spectrum of microRNAs in metabolic disease that complement studies reporting physiologic roles for microRNAs in the regulation of glucose and lipid metabolism in animal models. We also discuss the emerging roles of exosomal microRNAs as endocrine signals to regulate lipid and carbohydrate metabolism.

Spectral matching RotD100 target spectra: Effect on records characteristics and seismic response

This article investigates three different approaches to generate seismic input compatible with RotD100 design spectra: (1) separately matching each horizontal component to the target spectrum, (2) separately matching and then scaling-down the records to improve the match and (3) directly pursuing the match of RotD100 by simultaneously modifying both horizontal components. We examine the strong motion characteristics of the resulting records individually and their variability as suites of input records. The records generated, along with a set of amplitude-scaled records, are used as input for bi-directional non-linear response history analyses of idealized single column reinforced concrete bridge piers with different geometric and reinforcement characteristics. It is shown that the records generated pursuing a direct match of the target spectrum attain the closest match, retain better the strong motion characteristics of the seed records and their horizontal components exhibit a spectral variability comparable to suites of amplitude-scaled records. Regarding the effect on seismic response, the suites constructed separately matching each component consistently imposed larger peak inelastic and total energy demands than all other suites. Directly pursuing the match of RotD100 generated responses close but consistently below the expected from amplitude-scaled suites. The best results were obtained using the direct match methodology but using as target 110% the RotD100 spectrum as required in ASCE 7-16.

Metamaterial Reverse Multiple Prediction Method Based on Deep Learning

Metamaterials and their related research have had a profound impact on many fields, including optics, but designing metamaterial structures on demand is still a challenging task. In recent years, deep learning has been widely used to guide the design of metamaterials, and has achieved outstanding performance. In this work, a metamaterial structure reverse multiple prediction method based on semisupervised learning was proposed, named the partially Conditional Generative Adversarial Network (pCGAN). It could reversely predict multiple sets of metamaterial structures that can meet the needs by inputting the required target spectrum. This model could reach a mean average error (MAE) of 0.03 and showed good generality. Compared with the previous metamaterial design methods, this method could realize reverse design and multiple design at the same time, which opens up a new method for the design of new metamaterials.

Case Study on the Fitting Method of Typical Objects

This study proposes different fitting methods for different types of targets in the 400–900 nm wavelength range, based on convex optimization algorithms, to achieve the effect of high-precision spectral reconstruction for small space-borne spectrometers. This article first expounds on the mathematical model in the imaging process of the small spectrometer and discretizes it into an AX = B matrix equation. Second, the design basis of the filter transmittance curve is explained. Furthermore, a convex optimization algorithm is used, based on 50 filters, and appropriate constraints are added to solve the target spectrum. First, in terms of spectrum fitting, six different ground object spectra are selected, and Gaussian fitting, polynomial fitting, and Fourier fitting are used to fit the original data and analyze the best fit of each target spectrum. Then the transmittance curve of the filter is equally divided, and the corresponding AX = B discrete equation set is obtained for the specific object target, and a random error of 1% is applied to the equation set to obtain the discrete spectral value. The fitting is performed for each case to determine the best fitting method with errors. Subsequently, the transmittance curve of the filter with the detector characteristics is equally divided, and the corresponding AX = B discrete equation set is obtained for the specific object target. A random error of 1% is applied to the equation set to obtain the error. After the discrete spectral values are obtained, the fitting is performed again, and the best fitting method is determined. In order to evaluate the fitting accuracy of the original spectral data and the reconstruction accuracy of the calculated discrete spectrum, the three evaluation indicators MSE, ARE, and RE are used for evaluation. To measure the stability and accuracy of the spectral reconstruction of the fitting method more accurately, it is necessary to perform 500 cycles of calculations to determine the corresponding MSE value and further analyze the influence of the fitting method on the reconstruction accuracy. The results show that different fitting methods should be adopted for different ground targets under the error conditions. The three indicators, MSE, ARE, and RE, have reached high accuracy and strong stability. The effect of high-precision reconstruction of the target spectrum is achieved. This article provides new ideas for related scholars engaged in hyperspectral reconstruction work and promotes the development of hyperspectral technology.

NT-CRISPR: Combining natural transformation and CRISPR/Cas9 counterselection for markerless and scarless genome editing in Vibrio natriegens

The fast-growing bacterium Vibrio natriegens has recently gained increasing attention as a novel chassis organism for a wide range of projects. To fully harness the potential of this fascinating bacterium, convenient and highly efficient genome editing methods are indispensable to create novel strains, tailored for specific applications. V. natriegens is able to take up free DNA and incorporate it into its genome by homologous recombination. This process, called natural transformation, was tamed for genome editing. It displays a high efficiency and is able to mediate uptake of multiple DNA fragments, thereby allowing multiple simultaneous edits. Here, we describe NT-CRISPR, a combination of natural transformation with CRISPR/Cas9 counterselection. In two temporally distinct steps, we first performed a genome edit by natural transformation and second, induced CRISPR/Cas9, targeting the wild type sequence, leading to death of non-edited cells. Through highly efficient cell killing with efficiencies of up to 99.999 %, integration of antibiotic resistance markers became dispensable and thus enabled scarless and markerless edits with single-base precision. We used NT-CRISPR for deletions, integrations and single-base modifications with editing efficiencies of up to 100 % and further demonstrated its applicability for the simultaneous deletion of multiple chromosomal regions. Lastly, we demonstrated that the near PAM-less Cas9 variant SpG Cas9 is compatible with NT-CRISPR and thereby massively broadens the target spectrum.

Spectral Matching of Three-Component Seismic Ground Accelerations for Nuclear Power Plants

The aim of the paper is to introduce a new procedure of three-component spectral matching of seismic ground acceleration records. The procedure is straightforward, yet it is general. In principle, the procedure involves varying of both the Fourier amplitude and the phase spectra so that the modified records’ spectra agree with a target. The matching can be performed against either a target Fourier or response spectra. In the former the solution is exact, while in the latter it becomes approximate. A target spectrum representative of three directions should be provided. In the example several three-component records were matched against two target spectra. Good convergence was achieved in velocity and displacement records so that no baseline correction was necessary. The couplings among the components were preserved.

Two-step machine learning enables optimized nanoparticle synthesis

In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

Bio-Engineered Nisin with Increased Anti-Staphylococcus and Selectively Reduced Anti-Lactococcus Activity for Treatment of Bovine Mastitis

Bovine mastitis is a significant economic burden for dairy enterprises, responsible for premature culling, prophylactic and therapeutic antibiotic use, reduced milk production and the withholding (and thus wastage) of milk. There is a desire to identify novel antimicrobials that are expressly directed to veterinary applications, do not require a lengthy milk withholding period and that will not have a negative impact on the growth of lactic acid bacteria involved in downstream dairy fermentations. Nisin is the prototypical lantibiotic, a family of highly modified antimicrobial peptides that exhibit potent antimicrobial activity against many Gram-positive microbes, including human and animal pathogens including species of Staphylococcus and Streptococcus. Although not yet utilized in the area of human medicine, nisin is currently applied as the active agent in products designed to prevent bovine mastitis. Over the last decade, we have harnessed bioengineering strategies to boost the specific activity and target spectrum of nisin against several problematic microorganisms. Here, we screen a large bank of engineered nisin derivatives to identify novel derivatives that exhibit improved specific activity against a selection of staphylococci, including mastitis-associated strains, but have unchanged or reduced activity against dairy lactococci. Three such peptides were identified; nisin A M17Q, nisin A T2L and nisin A HTK.