Silver nanoparticles (AgNPs) are used in food packaging materials, dental care products and other consumer goods and can result in oral exposure. To determine whether AgNP coatings modulate transcriptional responses to AgNP exposure, we exposed mice orally to 20 nm citrate (cit)-coated AgNPs (cit-AgNPs) or polyvinylpyrrolidone (PVP)-coated AgNPs (PVP-AgNPs) at a 4 mg/kg dose for 7 consecutive days and analyzed changes in the expression of protein-coding genes and long noncoding RNAs (lncRNAs), a new class of regulatory RNAs, in the liver. We identified unique and common expression signatures of protein-coding and lncRNA genes, altered biological processes and signaling pathways, and coding-non-coding gene interactions for cit-AgNPs and PVP-AgNPs. Commonly regulated genes comprised only about 10 and 20 percent of all differentially expressed genes in PVP-AgNP and cit-AgNP exposed mice, respectively. Commonly regulated biological processes included glutathione metabolic process and cellular oxidant detoxification. Commonly regulated pathways included Keap-Nrf2, PPAR, MAPK and IL-6 signaling pathways. The coding-non-coding gene co-expression analysis revealed that proteincoding genes were co-expressed with a variable number of lncRNAs ranging from one to twenty three and may share functional roles with the protein-coding genes. PVP-AgNP exposure induced a more robust transcriptional response than cit-AgNP exposure characterized by more than twofold higher number of differentially expressed both protein-coding and lncRNA genes. Our data demonstrate that the surface coating strongly modulates the spectrum and the number of

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There are several variants of artificial intelligence (AI) hardware structures which are under study by the semiconductor industry as potential future synergistic technology adders to existing complementary metal–oxide–semiconductor (CMOS) designs. This paper will discuss some of the failure analysis challenges which have appeared in discrete test structures and test arrays of an exploratory PCM program at IBM's Albany AI Hardware Research Center.

Power consumption of conventional CMOS semiconductor architectures has grown to the point where novel structures need to be introduced to mitigate the power load within the chip. The introduction of the specialized artificial intelligence devices goes hand in hand with the inception of novel materials and processes into conventional semiconductor fabrication, which drives the need for expanding the host of failure analysis techniques and diagnostic capabilities. This paper describes a case study of elemental transmission electron microscopy tomography on an exploratory phase change memory test structure and comments upon some technique observations: advantages and disadvantages.