Photoactivatable Plant Hormone-Based Chemical Inducers of Proximity for Applications.

Protein interactions play a crucial role in regulating cellular mechanisms, highlighting the need for effective methods to control these processes. In this regard, chemical inducers of proximity (CIPs) offer a promising approach to precisely manipulate protein-protein interactions in live cells and . In this study, we introduce pMandi, a photocaged version of the plant hormone-based CIP mandipropamid (Mandi), which allows the use of light as an external trigger to induce protein proximity in live mammalian cells.

Integer Charge Transfer Model-PTCDA on MgO(001)/Ag(001) Probing the Transition from Single to Double Integer Charge Transfer.

For weakly interacting adsorbate/substrate systems, the integer charge transfer (ICT) model describes how charge transfer across interfaces depends on the substrate work function. In particular, work function regimes where no charge transfer occurs (vacuum level alignment) can be distinguished from regions where integer charge transfer by electron tunneling from substrate to adsorbate or vice versa takes place (Fermi level pinning). While the formation of singly integer charged molecular anions and cations of organic semiconductors on various substrates has been well described by this model, the double integer charging regime has so far remained unexplored and experimentally elusive.

Out-of-Pocket Costs Burden in Marketplace Plans for People With Diabetes.

Background: The Affordable Care Act (ACA) aims to expand coverage and increase access to health insurance. Despite the increase of insured individuals, there are a number of concerns about whether coverage and care are affordable. Prior studies document a growing concern with rising premiums and cost-sharing, including deductibles, particularly for those with chronic conditions.

Latent Variable Forests for Latent Variable Score Estimation.

We develop a (LV Forest) algorithm for the estimation of latent variable scores with one or more latent variables. LV Forest estimates unbiased latent variable scores based on (CFA) models with ordinal and/or numerical response variables. Through parametric model restrictions paired with a nonparametric tree-based machine learning approach, LV Forest estimates latent variable scores using models that are unbiased with respect to relevant subgroups in the population.