A probabilistic approach for approximation of optical and opto-electronic properties of an opto-semiconductor wafer under consideration of measuring inaccuracy and model uncertainty

This paper presents a probabilistic machine learning approach to approximate wavelength values for unmeasured positions on an opto-semiconductor wafer after epitaxy. Insufficient information about optical and opto-electronic properties may lead to undetected specification violations and, consequently, to yield loss or may cause product quality issues. Collection of information is restricted because physical measuring points are expensive and in practice samples are only drawn from 120 specific positions. The purpose of the study is to reduce the risk of uncertainties caused by sampling and measuring inaccuracy and provide reliable approximations. Therefore, a Gaussian process regression is proposed which can determine a point estimation considering measuring inaccuracy and further quantify estimation uncertainty. For evaluation, the proposed method is compared with radial basis function interpolation using wavelength measurement data of 6-inch InGaN wafers. Approximations of these models are evaluated with the root mean square error. Gaussian process regression with radial basis function kernel reaches a root mean square error of 0.814 nm averaged over all wafers. A slight improvement to 0.798 nm could be achieved by using a more complex kernel combination. However, this also leads to a seven times higher computational time. The method further provides probabilistic intervals based on means and dispersions for approximated positions

In vitro rescue of FGA deletion by lentiviral transduction of afibrinogenemic patient's hepatocytes

Congenital afibrinogenemia is a rare inherited autosomal recessive disorder in which a mutation in one of three genes coding for the fibrinogen polypeptide chains Aα, Bβ or γ results in the absence of a functional coagulation protein. A patient with congenital afibrinogenemia, due to a FGA homozygous gene deletion, underwent an orthotopic liver transplantation that resulted in complete restoration of normal hemostasis. The patient's explanted liver provided a unique opportunity to further investigate a potential novel treatment modality

HIF-1α is a protective factor in conditional PHD2 deficient mice suffering from severe HIF-2α-induced excessive erythropoiesis

Erythropoiesis must be tightly balanced in order to guarantee adequate oxygen delivery to all tissues in the body. This process relies predominantly on the hormone erythropoietin (EPO) and its transcription factor hypoxia inducible factor (HIF). Accumulating evidence suggests that oxygen-sensitive prolyl hydroxylases (PHDs) are important regulators of this entire system. Here, we describe a novel mouse line with conditional PHD2 inactivation (cKO P2) in renal EPO producing cells, neurons and astrocytes that displayed excessive erythrocytosis due to severe over-production of EPO, exclusively driven by HIF-2α. In contrast, HIF-1α served as a protective factor, ensuring survival of cKO P2 mice with hematocrit values up to 86%. Using different genetic approaches, we show that simultaneous inactivation of PHD2 and HIF-1α resulted in a drastic PHD3 reduction with consequent overexpression of HIF-2α-related genes, neurodegeneration and lethality. Taken together, our results demonstrate for the first time that conditional loss of PHD2 in mice leads to HIF-2α-dependent erythrocytosis, whereas HIF-1α protects these mice, providing a platform for developing new treatments of EPO-related disorders like anemia