Understanding the metabolic burden of recombinant antibody production in Saccharomyces cerevisiae using a quantitative metabolomics approach

The cellular changes induced by heterologous protein expression in the yeast Saccharomyces cerevisiae have been analysed on many levels and found to be significant. However, even though high‐level protein production poses a metabolic burden, evaluation of the expression host at the level of the metabolome has often been neglected. We present a comparison of metabolite profiles of a wild‐type strain with those of three strains producing recombinant antibody variants of increasing size and complexity: an scFv fragment, an scFv–Fc fusion protein and a full‐length IgG molecule. Under producing conditions, all three recombinant strains showed a clear decrease in growth rate compared with the wild‐type strain and the severity of the growth phenotype increased with size of the protein. The levels of 76 intracellular metabolites were determined using a targeted (semi) quantitative mass spectrometry based approach. Based on unsupervised and supervised multivariate analysis of metabolite profiles, together with pathway activity profiling, the recombinant strains were found to be significantly different from each other and from the wild‐type strain. We observed the most prominent changes in metabolite levels for metabolites involved in amino acid and redox metabolism. Induction of the unfolded protein response was detected in all producing strains and is considered to be a contributing factor to the overall metabolic burden on the cells.     

Innovations and productivity: the shift during the 2008 crisis

This paper investigates how the recent crisis of 2008 changed relations between innovation and firm performance in Western Europe. We apply a structural framework of CDM modelling, which incorporates different stages of the knowledge creation process and takes into account the complex nature of innovations. The study is based on a balanced panel data of 420 listed manufacturing firms from the U.K., Germany and France. All the information is gathered from common sources, thereby reducing subjectivity, a typical problem in the field. We found, the crisis resulted in appreciable changes in the model. The most important evidence is enhancement of the role of firm resources in the post-recession period. We also reveal larger barriers for innovations, increased uncertainty and lower state dependence in R&D engagement, product creation and economic performance. These results could indicate the ‘cleaning effect’ of the crisis, which has worsened the business environment and enhanced competition.     

On the existence and origin of sluggish diffusion in chemically disordered concentrated alloys

Concentrated single phase solid solutions, including medium- and high-entropy alloys, represent a new class of materials that have recently attracted significant interest due to exceptional functional and structural properties. Their fascinating properties are mainly attributed to the sluggish atomic-level diffusion and transport, but its controlling mechanisms are largely unknown and there is certain skepticism about its very existence. By using microsecond-scale molecular dynamics, on-the-fly and conventional kinetic Monte Carlo, we reveal the governing role of percolation effects and composition dependence of the vacancy migration energy in diffusion. Surprisingly, an increase of concentration of faster species (Fe) in face-centered cubic Ni-Fe alloy may decrease the overall atomic diffusion. Consequently, the composition dependence of tracer diffusion coefficient has a minimum near the site percolation threshold, ～20?at.%Fe. We argue that this coupled percolation and composition-dependent barriers for vacancy jumps within different subsystems in medium- and high-entropy alloys leads, indeed, to the sluggish diffusion. A fast method for preselecting materials with potentially desired properties is suggested.     

Bayesian data analysis to quantify the uncertainty of intact rock strength

One of the main difficulties in the geotechnical design process lies in dealing with uncertainty. Uncertainty is associated with natural variation of properties, and the imprecision and unpredictability caused by insufficient information on parameters or models. Probabilistic methods are normally used to quantify uncertainty. However, the frequentist approach commonly used for this purpose has some drawbacks.First, it lacks a formal framework for incorporating knowledge not represented by data. Second, it has limitations in providing a proper measure of the confidence of parameters inferred from data. The Bayesian approach offers a better framework for treating uncertainty in geotechnical design. The advantages of the Bayesian approach for uncertainty quantification are highlighted in this paper with the Bayesian regression analysis of laboratory test data to infer the intact rock strength parameters σ_(ci) and m_i used in the Hoek-Brown strength criterion. Two case examples are used to illustrate different aspects of the Bayesian methodology and to contrast the approach with a frequentist approach represented by the nonlinear least squares(NLLS) method. The paper discusses the use of a Student's t-distribution versus a normal distribution to handle outliers, the consideration of absolute versus relative residuals, and the comparison of quality of fitting results based on standard errors and Bayes factors. Uncertainty quantification with confidence and prediction intervals of the frequentist approach is compared with that based on scatter plots and bands of fitted envelopes of the Bayesian approach. Finally, the Bayesian method is extended to consider two improvements of the fitting analysis. The first is the case in which the Hoek-Brown parameter, a, is treated as a variable to improve the fitting in the triaxial region. The second is the incorporation of the uncertainty in the estimation of the direct tensile strength from Brazilian test results within the overall evaluation of the intact rock strength.     

Distribution of Magnetic Flux in a Ferromagnetic Material in the Presence of a Nonferromagnetic Gap in the Composite Circuit

Russian Journal of Nondestructive Testing - Comparative analysis of the distribution of magnetic-induction vector components over the volume of a bulk homogeneous sample of 40Kh steel has been...     

Role of Co in formation of Ni-Ti clusters in maraging stainless steel

The effect of Co addition on the formation of Ni-Ti clusters in maraging stainless steel was studied by three dimensional atom probe (3DAP) and first-principles calculation. The cluster analysis based on the maximum separation approach showed an increase in size but a decrease in density of Ni-Ti clusters with increasing the Co content. The first-principles calculation indicated weaker Co-Ni (Co-Ti) interactions than Co-Ti (Fe-Ti) interactions, which should be the essential reason for the change of distribution characteristics of Ni-Ti clusters in bcc Fe caused by Co addition.     

Multicellular Tumor Spheroids (MCTS) as a 3D In Vitro Evaluation Tool of Nanoparticles

Multicellular tumor spheroid models (MCTS) are often coined as 3D in vitro models that can mimic the microenvironment of tissues. MCTS have gained increasing interest in the nano‐biotechnology field as they can provide easily accessible information on the performance of nanoparticles without using animal models. Considering that many countries have put restrictions on animals testing, which will only tighten in the future as seen by the recent developments in the Netherlands, 3D models will become an even more valuable tool. Here, an overview on MCTS is provided, focusing on their use in cancer research as most nanoparticles are tested in MCTS for treatment of primary tumors. Thereafter, various types of nanoparticles—from self‐assembled block copolymers to inorganic nanoparticles, are discussed. A range of physicochemical parameters including the size, shape, surface chemistry, ligands attachment, stability, and stiffness are found to influence nanoparticles in MCTS. Some of these studies are complemented by animal studies confirming that lessons from MCTS can in part predict the behaviour in vivo. In summary, MCTS are suitable models to gain additional information on nanoparticles. While not being able to replace in vivo studies, they can bridge the gap between traditional 2D in vitro studies and in vivo models.     

Improving weld formability by a novel dual-rotation bobbin tool friction stir welding

A novel dual-rotation bobbin tool friction stir welding (DBT-FSW) was developed, in which the upper shoulder (US) and lower shoulder (LS) have different rotational speeds. This process was tried to weld 3.2 mm thick aluminum-lithium alloy sheets. The metallographic analysis and torque measurement were carried out to characterize the weld formability. Experimental results show that compared to conventional bobbin tool friction stir welding, the DBT-FSW has an excellent process stability, and can produce the defect-free joints in a wider range of welding parameters. These can be attributed to the significant improvement of material flow caused by the formation of a staggered layer structure and the unbalanced force between the US and LS during the DBT-FSW process.     

Investigating the Dynamic Characteristics of High-Temperature SOI CMOS VLSIC Elements

The static and dynamic characteristics of SOI CMOS inverters are investigated in the temperature range from–60 to 250°С. The experimental dependences of the dynamic and static currents, as well as the delay of the logic gate (inverter), on temperature are obtained. Based on these characteristics, the operability of SOI CMOS logic gates at high temperatures is estimated.