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Paul Lang

Researcher at Merck Serono

Publications -  10
Citations -  964

Paul Lang is an academic researcher from Merck Serono. The author has contributed to research in topics: Phosphorylation & Receptor. The author has an hindex of 6, co-authored 9 publications receiving 929 citations. Previous affiliations of Paul Lang include University College London & National Jewish Health.

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Journal Article

Human and mouse killer-cell inhibitory receptors recruit PTP1C and PTP1D protein tyrosine phosphatases.

TL;DR: A common pathway by which diverse KIR can down-regulate NK and T cell activation programs is document and the sequence of the immunoreceptor tyrosine-based inhibitory motif (ITIM) is defined, initially described in FcgammaRIIB1, and expressed in both human and mouse KIR.
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Cellular imaging in drug discovery

TL;DR: This review describes how cellular imaging technologies contribute to the drug discovery process and addresses both high-content and high-throughput needs.
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Antigens Varying in Affinity for the B Cell Receptor Induce Differential B Lymphocyte Responses

TL;DR: Biochemical analysis revealed that at saturating ligand concentrations the ability of phage to stimulate some early signaling responses, such as Ca++ mobilization and tyrosine phosphorylation of syk or Igα, was highly affinity dependent, whereas the ability to stimulate Lyn phosphorylated was less so, suggesting that the BCR is capable of differential signaling.
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Vascular endothelial growth factor stimulates dephosphorylation of the catenins p120 and p100 in endothelial cells.

TL;DR: It is found that VEGF potently stimulated a rapid and dose-dependent decrease in serine/threonine phosphorylation of p120 and p100, raising the possibility that the dephosphorylated of p 120 and p 100 triggered by VEGf may contribute to mechanisms regulating permeability and/or motility through modulation of cadherin adhesiveness.
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Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay.

TL;DR: A hit selection procedure based on machine learning methods is introduced and it is demonstrated that this method increases the hit verification rate significantly (up to a factor of 5), compared to conventional hit selection based on single readouts only.