Abstract: Methods, systems, and apparatuses include determining a set of data. The set of data includes multiple numerical ranges associated with an embedding and an attribute. The numerical range is sampled to obtain a sample value which is also associated with the embedding and the attribute. A set of sample value training data is generated, the set including the sample value, the associated embedding, and the associated attribute. A trained neural network prediction model is generated by applying a prediction model to the set of sample value training data. A set of input data is applied to the trained neural network prediction model. An output is determined by the trained neural network prediction model based on the set of input data. The output is a predicted range of values based on an output mean and an output standard deviation.

Abstract: Provided are DNAs comprising a polynucleotide that encodes at least a first modified miR-30 precursor and a second modified miR-30 precursor. Also provided are vectors comprising the the DNAs, where the vector can replicate in a host cell. Additionally, specific lentiviral vectors comprising the above-described DNA are provided, as are methods of inhibiting expression of a target gene in a eukaryotic cell.

Abstract: Provided are DNAs comprising a polynucleotide that encodes at least a first modified miR-30 precursor and a second modified miR-30 precursor. Also provided are vectors comprising the the DNAs, where the vector can replicate in a host cell. Additionally, specific lentiviral vectors comprising the above-described DNA are provided, as are methods of inhibiting expression of a target gene in a eukaryotic cell.