Abstract-Over the past few decades, a large family of algorithms-supervised or unsupervised; stemming from statistics or geometry theory-has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.

The use of multiwalled carbon nanotubes as a platinum support for proton exchange membrane fuel cells has been investigated as a way to reduce the cost of fuel cells through an increased utilization of platinum. Carbon nanotubes were selectively grown directly on the carbon paper by chemical vapor deposition with electrodeposited cobalt catalyzing the growth of the carbon nanotubes. The as-prepared carbon nanotubes were employed as the support for the subsequent platinum catalyst, which is electrodeposited on the carbon nanotubes. Physicochemical and electrochemical characterizations were conducted to identify the morphologies of the cobalt, the carbon nanotubes, and the electrodeposited platinum on the carbon nanotubes. The feasibility of a fuel cell using the carbon nanotube-based electrodes was demonstrated.

While remdesivir has garnered much hope for its moderate anti-Covid-19 effects, its parent nucleoside, GS-441524, has been overlooked. Pharmacokinetic analysis of remdesivir evidences premature serum hydrolysis to GS-441524; GS-441524 is the predominant metabolite reaching the lungs. With its synthetic simplicity and in vivo efficacy in the veterinary setting, we contend that GS-441524 is superior to remdesivir for Covid-19 treatment.

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A hollow-structured and highly ordered mesoporous aluminosilicate with a 3D pore network and significantly improved hydrothermal stability has been successfully prepared by a simple method. To elucidate its novel structure and improved hydrothermal stability, a model for the formation of such a hollow spherical material has been proposed.

Inhibiting glycolysis remains an aspirational approach for the treatment of cancer. We previously identified a subset of cancers harboring homozygous deletion of the glycolytic enzyme Enolase (ENO1) with exceptional sensitivity to inhibition of its redundant paralogue, ENO2, through a therapeutic strategy known as collateral lethality. Here, we show that a small molecule Enolase inhibitor, POMHEX, can selectively kill ENO1 -deleted glioma cells at low nanomolar concentrations and eradicate intracranial orthotopic ENO1 -deleted tumors in mice at doses well-tolerated in non-human primates. Our data provide in vivo proof-of-principal for the power of collateral lethality in precision oncology and demonstrate the utility of POMHEX for glycolysis inhibition with potential across a range of therapeutic settings.

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