Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

Background: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement.

Methods: This was a secondary analysis of the Global Appraisal of Individual Needs (GAIN) dataset and United States Census Bureau data utilizing random forest machine learning and generalized linear mixed modelling.

Finite element simulations of electrostatic dopant potentials in thin semiconductor specimens for electron holography.

Two-dimensional finite element simulations of electrostatic dopant potentials in parallel-sided semiconductor specimens that contain p-n junctions are used to assess the effect of the electrical state of the surface of a thin specimen on projected potentials measured using off-axis electron holography in the transmission electron microscope. For a specimen that is constrained to have an equipotential surface, the simulations show that the step in the projected potential across a p-n junction is always lower than would be predicted from the properties of the bulk device, but is relatively insensitive to the value of the surface state energy, especially for thicker specimens and higher dopant concentrations. The depletion width measured from the projected potential, however, has a complicated dependence on specimen thickness.

Factors of activation and stabilization of DNA-methylases from Shigella sonnei 47 and Mycobacterium smegmatis butyricum cells.

A comparative study of factors of activation and stabilization of individual DNA-methylases from two bacterial strains--Shigella sonnei 47 and Mycobacterium smegmatis butyricum--isolated by isoelectrofocusing in a pH gradient has been carried out. Storage of enzymes at +4 degrees C (pH 7.5) is accompanied by periodic changes in the methylating activity.

Sequence specificity of isolated DNA-adenine methylases from Mycobacterium smegmatis (butyricum) and Shigella sonnei 47 cells.

A set of four individual DNA-adenine methylases differing in pI (isoelectric point) values (MMbu4.2, MMbu6.4, MMbu7.

Isoelectric focusing of bacterial DNA methylases.

The multiplicity of bacterial DNA methylases has been shown for new microorganisms, Mycobacteria and Shigella, by a double-step procedure including column chromatography followed by isoelectric focusing of the total methylase fraction. The profiles of the DNA methylating activity of Sh. sonnei 47 and M.