Abstract: Genetically modified microorganisms that have the ability to convert carbon substrates into chemical products such as isobutanol are disclosed. For example, genetically modified methanotrophs that are capable of generating isobutanol at high titers from a methane source are disclosed. Methods of making these genetically modified microorganisms and methods of using them are also disclosed.

Abstract: The current disclosure relates to methods, compositions and kits for detecting modified adenosine in a target RNA molecule. Aspects relate to a method for detecting modified adenosine in a target ribonucleic acid (RNA) comprising contacting the target RNA with an adenosine deaminase enzyme (adenosine deaminase, RNA-specific) to generate a target RNA with deaminated adenosines and sequencing the target RNA with deaminated adenosines; wherein the modified adenosine is detected when the nucleotide sequence includes adenosine within a m6A motif.

Abstract: Genetically modified microorganisms that have the ability to convert carbon substrates into chemical products such as 2,3-BDO; 1,4-BDO; isobutyraldehyde; isobutanol; 1-butanol; n-butanol; ethanol; fatty alcohols; and fatty acid methyl ester are disclosed. For example, genetically modified methanotrophs that are capable of generating 2,3-BDO; 1,4-BDO; isobutyraldehyde; isobutanol; 1-butanol; n-butanol; ethanol; fatty alcohols; and fatty acid methyl ester at high titers from a methane source are disclosed. Methods of making these genetically modified microorganisms and methods of using them are also disclosed. These microorganisms and methods make use of molecular switches to regulate gene expression.

Abstract: An asset-exchange feedback system is implemented for performing asset-exchange feedback operations. The asset-exchange feedback system collects historical asset-listing data from an asset-exchange platform. The historical asset-listing data comprises, for each asset listing of a plurality of previous asset listings, a plurality of asset-listing attributes and a result of the asset listing. The asset-exchange feedback system uses a first machine learning model to determine, based on the historical asset-listing data, a first set of attribute-importance scores. Each attribute-importance score in the first set of attribute-importance scores corresponds to a respective asset-listing attribute in the plurality of asset-listing attributes and indicates an importance of the respective asset-listing attribute to one or more offerees participating in the asset-exchange platform. The asset-exchange feedback system performs an asset-exchange feedback operation based on the first set of attribute-importance scores.