The effects of sequence length and composition of random sequence peptides on the growth of E. coli cells

We study the potential for the de novo evolution of genes from random nucleotide sequences using libraries of E. coli expressing random sequence peptides. We assess the effects of such peptides on cell growth by monitoring frequency changes in individual clones in a complex library through four serial passages. Using a new analysis pipeline that allows the tracing of peptides of all lengths, we find that over half of the peptides have consistent effects on cell growth. Across nine different experiments, around 16 of clones increase in frequency and 36 decrease, with some variation between individual experiments. Shorter peptides (8ndash;20 residues), are more likely to increase in frequency, longer ones are more likely to decrease. GC content, amino acid composition, intrinsic disorder, and aggregation propensity show slightly different patterns between peptide groups. Sequences that increase in frequency tend to be more disordered with lower aggregation propensity. This coincides with the observation that young genes with more disordered structures are better tolerated in genomes. Our data indicate that random sequences can be a source of evolutionary innovation, since a large fraction of them are well tolerated by the cells or can provide a growth advantage

Genome-wide acceleration of protein evolution in flies (Diptera)

BACKGROUND: The rate of molecular evolution varies widely between proteins, both within and among lineages. To what extent is this variation influenced by genome-wide, lineage-specific effects? To answer this question, we assess the rate variation between insect lineages for a large number of orthologous genes. RESULTS: When compared to the beetle Tribolium castaneum, we find that the stem lineage of flies and mosquitoes (Diptera) has experienced on average a 3-fold increase in the rate of evolution. Pairwise gene comparisons between Drosophila and Tribolium show a high correlation between evolutionary rates of orthologous proteins. CONCLUSION: Gene specific divergence rates remain roughly constant over long evolutionary times, modulated by genome-wide, lineage-specific effects. Among the insects analysed so far, it appears that the Tribolium genes show the lowest rates of divergence. This has the practical consequence that homology searches for human genes yield significantly better matches in Tribolium than in Drosophila. We therefore suggest that Tribolium is better suited for comparisons between phyla than the widely employed dipterans

Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data

Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data

The Financial Benefits of Various Catastrophic Failure Prevention Strategies in a Wind Farm: Two market studies (UK-Spain)

Operation of wind farms is driven by the overall aim of minimising costs while maximising energy sales. However, in certain circumstances investments are required to guarantee safe operation and survival of an asset. In this paper, we discuss the merits of various catastrophic failure prevention strategies in a Spanish wind farm. The wind farm operator was required to replace blades in two phases: temporary and final repair. We analyse the power performance of the turbine in the different states and investigate four scenarios with different timing of temporary and final repair during one year. The financial consequences of the scenarios are compared with a baseline by using a discounted cash flow analysis that considers the wholesale electricity market selling prices and interest rates. A comparison with the UK electricity market is conducted to highlight differences in the rate of return in the two countries

Quantification of finite-temperature effects on adsorption geometries of π\pi-conjugated molecules

The adsorption structure of the molecular switch azobenzene on Ag(111) is investigated by a combination of normal incidence x-ray standing waves and dispersion-corrected density functional theory. The inclusion of non-local collective substrate response (screening) in the dispersion correction improves the description of dense monolayers of azobenzene, which exhibit a substantial torsion of the molecule. Nevertheless, for a quantitative agreement with experiment explicit consideration of the effect of vibrational mode anharmonicity on the adsorption geometry is crucial.Comment: 12 pages, 3 figure

Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements

Operation and Maintenance (O&M) is an important cost driver of modern wind turbines. Condition monitoring (CM) allows the implementation of predictive O&M strategies helping to reduce costs. In this work a novel approach for wind turbine condition monitoring is proposed focusing on synergistic effects of coexisting sensing technologies. The main objective is to understand the predictability of signals using information from other measurements recorded at different locations of the turbine. The approach is based on a multi-step procedure to pre-process data, train a set of conversion functions and evaluate their performance. A subsequent sensitivity analysis measuring the impact of the input variables on the predicted response reveals hidden relationships between signals. The concept feasibility is tested in a case study using Supervisory Control And Data Acquisition (SCADA) data from an offshore turbine

A new procedure for microarray experiments to account for experimental noise and the uncertainty of probe response

Although microarrays are routine analysis tools in biomedical research, theystill yield noisy output that often requires experimental confirmation. Manystudies have aimed at optimizing probe design and statistical analysis totackle this problem. However, less emphasis has been placed on controlling thenoise inherent to the experimental approach. To address this problem, weinvestigate here a procedure that controls for such experimental variance andcombine it with an assessment of probe performance. Two custom arrays were usedto evaluate the procedure: one based on 25mer probes from an Affymetrix designand the other based on 60mer probes from an Agilent design. To assessexperimental variance, all probes were replicated ten times. To assess probeperformance, the probes were calibrated using a dilution series of targetmolecules and the signal response was fitted to an absorption model. We foundthat significant variance of the signal could be controlled by averaging acrossprobes and removing probes that are nonresponsive. Thus, a more reliable signalcould be obtained using our procedure than conventional approaches. We suggestthat once an array is properly calibrated, absolute quantification of signalsbecomes straight forward, alleviating the need for normalization and referencehybridizations.<br