Preclinical pharmacology of RA15127343: In vitro and in vivo activity of a novel ultralong-acting basal insulin.

Aim: To report the in vitro and in vivo preclinical pharmacokinetic (PK) and pharmacodynamic (PD) properties of RA15127343, a novel ultralong-acting insulin analogue targeting once-weekly administration, in female Göttingen minipigs.

Methods: In vitro binding and activation of human insulin receptor isoforms (IR-A/IR-B), glucose uptake in rat myocytes, as well as mitogenic activity of RA15127343 were evaluated. In vivo, the PK and PD activities of RA15127343 were assessed in female, normoglycaemic Göttingen minipigs.

Ultracold Bose Gases in Dynamic Disorder with Tunable Correlation Time.

We study experimentally the dissipative dynamics of ultracold bosonic gases in a dynamic disorder potential with tunable correlation time. First, we measure the heating rate of thermal clouds exposed to the dynamic potential and present a model of the heating process, revealing the microscopic origin of dissipation from a thermal, trapped cloud of bosons. Second, for Bose-Einstein condensates, we measure the particle loss rate induced by the dynamic environment.

Impacts of COVID-19 on global poverty, food security, and diets: Insights from global model scenario analysis.

This study assesses the impact of coronavirus disease 2019 (COVID-19) on poverty, food insecurity, and diets, accounting for the complex links between the crisis and the incomes and living costs of vulnerable households. Key elements are impacts on labor supply, effects of social distancing, shifts in demand from services involving close contact, increases in the cost of logistics in food and other supply chains, and reductions in savings and investment. These are examined using IFPRI's global general equilibrium model linked to epidemiological and household models.

Agricultural subsidies and global greenhouse gas emissions.

Agricultural production is strongly affected by and a major contributor to climate change. Agriculture and land-use change account for a quarter of total global emissions of greenhouse gases (GHG). Agriculture receives around US$600 billion per year worldwide in government support.

Demystifying autofluorescence with excitation-scanning hyperspectral imaging.

Autofluorescence has historically been considered a nuisance in medical imaging. Many endogenous fluorophores, specifically, collagen, elastin, NADH, and FAD, are found throughout the human body. Diagnostically, these signals can be prohibitive since they can outcompete signals introduced for diagnostic purposes.

Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning.

Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image.

Using neural networks for (13)c NMR chemical shift prediction-comparison with traditional methods.

Interpretation of (13)C chemical shifts is essential for structure elucidation of organic molecules by NMR. In this article, we present an improved neural network approach and compare its performance to that of commonly used approaches. Specifically, our recently proposed neural network (J.

Validation of structural proposals by substructure analysis and 13C NMR chemical shift prediction.

The 2D NMR-guided computer program COCON can be extremely valuable for the constitutional analysis of unknown compounds, if its results are evaluated by neural network-assisted 13C NMR chemical shift and substructure analyses. As instructive examples, data sets of four differently complex marine natural products were thoroughly investigated. As a significant step towards a true automated structure elucidation, it is shown that the primary COCON output can be safely diminished to less than 1% of its original size without losing the correct structural proposal.

Genius: a genetic algorithm for automated structure elucidation from 13C NMR spectra.

The automated structure elucidation of organic molecules from experimentally obtained properties is extended by an entirely new approach. A genetic algorithm is implemented that uses molecular constitution structures as individuals. With this approach, the structure of organic molecules can be optimized to meet experimental criteria, if in addition a fast and accurate method for the prediction of the used physical or chemical features is available.