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Recently, the World Health Organization confirmed 120 new human cases of avian H7N9 influenza in China resulting in 37 deaths, highlighting the concern for a potential pandemic and the need for an effective, safe, and high-speed vaccine production platform. Production speed and scale of mRNA-based vaccines make them ideally suited to impede potential pandemic threats. Here we show that lipid nanoparticle (LNP)-formulated, modified mRNA vaccines, encoding hemagglutinin (HA) proteins of H10N8 (A/Jiangxi-Donghu/346/2013) or H7N9 (A/Anhui/1/2013), generated rapid and robust immune responses in mice, ferrets, and nonhuman primates, as measured by hemagglutination inhibition (HAI) and microneutralization (MN) assays. A single dose of H7N9 mRNA protected mice from a lethal challenge and reduced lung viral titers in ferrets. Interim results from a first-in-human, escalating-dose, phase 1 H10N8 study show very high seroconversion rates, demonstrating robust prophylactic immunity in humans. Adverse events (AEs) were mild or moderate with only a few severe and no serious events. These data show that LNP-formulated, modified mRNA vaccines can induce protective immunogenicity with acceptable tolerability profiles.

Entrepreneurs play a fundamental role in bringing new technologies to market. Because technologies are often configurable to serve a variety of different markets, it is possible for entrepreneurs to identify multiple market opportunities prior to the first market entry of their emerging firms, and if they elect to do so, to therefore have a choice of which market to enter first. The empirical results presented in this paper offer three new insights regarding this important early-stage choice in new firm creation. First, they reveal that serial entrepreneurs have learned through prior start-up experience to generate a "choice set" of alternative market opportunities before deciding which one to pursue in their new firm creation. Second, the analysis indicates that entrepreneurs who identify a "choice set" of market opportunities prior to first entry derive performance benefits by doing so. Third, the positive relationship between the number of market opportunities identified prior to first entry and new firm performance is nonlinear and subject to decreasing marginal return. The research literature has yet to acknowledge the notion of multiple opportunity identification prior to entry, and the related idea of selecting the most favorable market opportunity for the creation of a new technology firm.market opportunities, serial entrepreneurs, technological commercialization, new firm creation

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The choice of the firm's market environment is one of the fundamental decisions of firm founders. We study the pre-entry generation of founders' market choice sets by investigating their search for market opportunities in which the firm's technological resources, as embodied in a product or service, can be commercialized. Analyzing data collected through personal interviews with founders of 496 technology ventures, we find that founding teams with more diverse industry experience and more diverse external knowledge sourcing relationships identify not only a larger number of but, in particular, more varied (distant) market opportunities. However, the extent to which strategic variety of such opportunities is identified depends on the founders' technological expertise, whereas technological expertise is less relevant in identification of the number of opportunities. Furthermore, by showing that the extent and nature of the firm's pre-entry opportunity set has a significant effect on the likelihood of subsequent firm diversification, we document how initial constraints in founders' choice sets can have a lasting impact on the growth potential that the new firm exploits over time. We discuss the implications of our findings for the literatures on organizational learning and innovation, entrepreneurship, as well as the strategy literature examining firm growth, diversification, and value creation.

Proximal tubule cells (PTCs), which are the primary site of kidney injury associated with ischemia or nephrotoxicity, are the site of oligonucleotide reabsorption within the kidney. We exploited this property to test the efficacy of siRNA targeted to p53, a pivotal protein in the apoptotic pathway, to prevent kidney injury. Naked synthetic siRNA to p53 injected intravenously 4 h after ischemic injury maximally protected both PTCs and kidney function. PTCs were the primary site for siRNA uptake within the kidney and body. Following glomerular filtration, endocytic uptake of Cy3-siRNA by PTCs was rapid and extensive, and significantly reduced ischemia-induced p53 upregulation. The duration of the siRNA effect in PTCs was 24 to 48 h, determined by levels of p53 mRNA and protein expression. Both Cy3 fluorescence and in situ hybridization of siRNA corroborated a short t 1 ⁄2 for siRNA. The extent of renoprotection, decrease in cellular p53 and attenuation of p53-mediated apoptosis by siRNA were dose-and time-dependent. Analysis of renal histology and apoptosis revealed improved injury scores in both cortical and corticomedullary regions. siRNA to p53 was also effective in a model of cisplatininduced kidney injury. Taken together, these data indicate that rapid delivery of siRNA to proximal tubule cells follows intravenous administration. Targeting siRNA to p53 leads to a dose-dependent attenuation of apoptotic signaling, suggesting potential therapeutic benefit for ischemic and nephrotoxic kidney injury.

The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here we propose a novel gene network reconstruction algorithm, derived from classic Bayesian network methods, that utilizes naturally occurring genetic variations as a source of perturbations to elucidate the network. This algorithm incorporates relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of this novel algorithm is demonstrated via application to liver gene expression data from a segregating mouse population. We demonstrate that the network derived from these data using our novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.

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