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To determine the effect of ionic liquids to candidate proteins, 1 μl of purified protein was added into a 199 μl reaction mix including: 0%, 5%, 10%, 15%, or 20% of ionic liquid; 0.625 mM (final concentration) of p -nitrophenyl substrates in buffers that has the optimal pH range for individual candidates. For control reactions, 1 μl of purified protein was replaced with 1 μl of buffer. Reactions were conducted under the optimal temperature of individual candidates for 12 h. After observation for signs of protein denaturation and precipitation, reactions were quenched by the addition of 50 μl 1 M NaCO 3 . Absorbance was read at 405 nm to determine the extent of conversion. Data were normalized for 100% response at the absence of ionic liquid.

Acknowledgements

Funding for this study was provided by The BioEnergy Science Center. The BioEnergy Science Center (BESC) and the Great Lakes Bioenergy Research Center (GLBRC) are U.S. Department of Energy Bioenergy Research Centers supported by the Office of Biological and Environmental Research in the DOE Office of Science. Metagenome sequencing of poplar biomass decaying community was performed at the Joint Genome Institute (JGI) under the auspices of the US Department of Energy's Office of Science, Biological and Environmental Research Program, and by the University of California, Lawrence Berkeley National Laboratory under contract No. DE-AC02-05CH11231, Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344, and Los Alamos National Laboratory under contract No. DE-AC02-06NA25396. We also thank Marie Thomas and James Wishart from the Chemistry department, Brookhaven National Laboratory, for kindly providing the ionic liquids for this study.

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PB, CD, and DAM are employed by Lucigen Corp., a company that manufactures cloning and expression products. The remaining authors declare that they have no competing interests.

L-LL participated in the design of experiments, coordination of this study, preformed most of experiments including the selection of candidate genes, all the gene cloning, full-length ORF identification (inverse PCR, DNA walking, and blast searches), SNP studies (sequence analyses and SNP identification, SNP clones construction, examine protein expression and solubility for each clone), sequencing, blast searching, and identifying ORF of all positive clones from the metagenomic expression library, recombinant plasmids construction, protein expression and purification, enzyme activity/property assays and characterization, and wrote the manuscript. Y-BZ participated in the protein purification for SNP studies. MGB performed DNA sequencing for SNP and DNA walking studies. SMM performed the Blastx searches and identified putative glycoside hydrolases from metagenome data. RB preformed the pH and temperature assays for enzyme no. 6. WSA participated in the experiment design of enzyme assays. MEH participated in the conceiving, design, and coordination of this study. PB, CD, and DAM preformed the expression library construction and screening. ST isolated high molecular weight metagenomic DNA, participated in the conceiving, design, and coordination of this study. SGT managed the metagenome sequencing. DvdL participated in the conceiving, design, and coordination of this study, and is the principal investigator of this study. All authors read and approved the final manuscript.

Forbes: How Alpha helps the Fortune 100 innovate at the speed of startups

With the rise of ‘lean’ methodologies literature, product managers today understand that continuously generating user feedback throughout the product lifecycle can help mitigate the risk of wasting resources to build something no one wants. In theory, user feedback can enable product managers to apply a data-driven approach to decision-making, so that only demanded features products get built.

The problem many encounter when deciding what product to build is, quite simply, that users don’t necessarily know what they want, so asking them is an exercise in futility. As folklore claims Henry Ford said (although most likely didn’t): “If I had asked my customers what they wanted they would have said a faster horse.”

There’s a scientific reason for that. is a phenomena caused by the human brain’s inability to predict how it will react in circumstances removed from the circumstances it is currently in. In the case of product management, users have trouble weighing the desirability of some theorized product with the feasibility of actually using the product.

Psychological Distance is Product Management

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Bridging Psychological Distance

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The Guide to Prototyping User Research Solutions

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Researchers from Georgetown University have conducted numerous studies illustrating this point. In one of their experiments, users were presented with the concepts of two different alarm clocks: one simple clock with the alarm being its only feature, the other – more complex with features like reading the weather traffic patterns to the user, among a dozen other features. Users invariably picked the product with the most features. However, when those products were actually produced sold, users invariably picked the simpler product. From a distance users prefer desirability, but up close .

Many organizations have turned to Minimum Viable Products as the solution to psychological distance. While MVPs do indeed solve the problem, they’re not an optimal solution for large organizations.

The goal of an MVP launch is not to launch the first version of a product. It’s simply to start gathering feedback observing users actually interacting with something. This distinction is critical.

Startups, by for whom the MVP concept was designed, are typically focused exclusively on a product users, can therefore launch iterate rapidly, sacrificing all other concerns. Larger organizations are more complex, so singularly focusing on iterating on live products is very difficult.

The larger the organization, the more people have a say in what must be a part of the feature set, which results in a complicated, all-too-familiar mess. But by arming product owners with the structure of an experiment, they can push back against feature-creep design by committee. Actual user data trumps Aeron-chair quarterbacking every time.

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