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Orien L. Tulp, PhD, MS, BS, CNS, FACN, celebrated for over 40 years of professional experience in his industry

Glioblastoma (GBM) is a disease of the whole brain, with infiltrative tumor cells protected by an intact blood-brain barrier (BBB). GBM has a poor prognosis despite aggressive treatment, in part due to the lack of adequate drug permeability at the BBB. Standard of care GBM therapies include radiation and cytotoxic chemotherapy that lead to DNA damage. Subsequent activation of DNA damage response (DDR) pathways can induce resistance. Various DDR inhibitors, targeting the key regulators of these pathways such as ataxia telangiectasia mutated and Rad3-related (ATR), are being explored as radio- and chemosensitizers. Elimusertib, a novel ATR kinase inhibitor, can prevent repair of damaged DNA, increasing efficacy of DNA-damaging cytotoxic therapies. Robust synergy was observed in vitro when elimusertib was combined with the DNA-damaging agent temozolomide; however, we did not observe improvement with this combination in in vivo efficacy studies in GBM orthotopic tumor-bearing mice. This in vitro–in vivo disconnect was explored to understand factors influencing central nervous system (CNS) distribution of elimusertib and reasons for lack of efficacy. We observed that elimusertib is rapidly cleared from systemic circulation in mice and would not maintain adequate exposure in the CNS for efficacious combination therapy with temozolomide. CNS distribution of elimusertib is partially limited by P-glycoprotein efflux at the BBB, and high binding to CNS tissues leads to low levels of pharmacologically active (unbound) drug in the brain. Acknowledging the potential for interspecies differences in pharmacokinetics, these data suggest that clinical translation of elimusertib in combination with temozolomide for treatment of GBM may be limited.

IEEE / Institute of Electrical and Electronics Engineers Incorporated

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Style transfer is to render given image contents in given styles, and it has an important role in both computer vision fundamental research and industrial applications. Following the success of deep learning based approaches, this problem has been re-launched recently, but still remains a difficult task because of trade-off between preserving contents and faithful rendering of styles. Indeed, how well-balanced content and style are is crucial in evaluating the quality of stylized images. In this paper, we propose an end-to-end two-stream Fully Convolutional Networks (FCNs) aiming at balancing the contributions of the content and the style in rendered images. Our proposed network consists of the encoder and decoder parts. The encoder part utilizes a FCN for content and a FCN for style where the two FCNs have feature injections and are independently trained to preserve the semantic content and to learn the faithful style representation in each. The semantic content feature and the style representation feature are then concatenated adaptively and fed into the decoder to generate style-transferred (stylized) images. In order to train our proposed network, we employ a loss network, the pre-trained VGG-16, to compute content loss and style loss, both of which are efficiently used for the feature injection as well as the feature concatenation. Our intensive experiments show that our proposed model generates more balanced stylized images in content and style than state-of-the-art methods. Moreover, our proposed network achieves efficiency in speed.

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Objective

To study the immunomodulatory effect of dimethyl fumarate (DF) on granulocyte macrophage colony-stimulating factor (GM-CSF) production in CD4⁺ T cells in experimental autoimmune encephalomyelitis (EAE) and human peripheral blood mononuclear cells (PBMCs).

IEEE / Institute of Electrical and Electronics Engineers Incorporated

IEEE / Institute of Electrical and Electronics Engineers Incorporated

IEEE Computer Society

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