Recombinant adeno-associated virus (rAAV) has become the vector of choice for the development of novel human gene therapies. High-yield manufacturing of high-quality vectors can be achieved using the baculovirus expression vector system. However, efficient production of rAAV in this insect cell-based system requires a genetic redesign of the viral protein 1 (VP1) operon. In this study, we generated a library of rationally designed rAAV serotype 5 variants with modulations in the translation-initiation region of VP1 and investigated the potency of the resulting vectors. We found that the initiation strength at the VP1 translational start had downstream effects on the VP2/VP3 ratio. Excessive incorporation of VP3 into a vector type decreased potency, even when the VP1/VP2 ratio was in balance. Finally, we successfully generated a potent rAAV vector based on serotype 5 with a balanced VP1/VP2/VP3 stoichiometry.
Power AND performativity AND process. Three concepts. Three theoretical perspectives that shape current studies of organizing, of organizations, how they come into existence and how they are maintained. Three distinct trajectories that have already been traced; their separate articulations creating tension, paradox, and contradiction. Can we resist existing conceptualizations to create new immanent relations? Can we dissolve the necessity of binary logics, of order, of finality, and embrace simultaneity and multiplicity? In this paper we reimagine the interplay between power and performativity, embracing the role of resistance within the emergent micro-processes of organizational becoming. To do this we take inspiration from the work of Gilles Deleuze.
That organizations exist in a fluid environment of unprecedented and discontinuous change seems beyond debate. We seem to find ourselves immersed in a world in which events have a tendency to unfold and overtake us in unforeseeable and novel ways that defy comprehension; a crisis of meaning takes place and conventional sensemaking is disrupted. Our need to imaginatively construct new meanings that allow us to understand what is going on and to work out how to respond becomes ever more pressing. We do live in interesting times. The emergence of the new, however, challenges current established ways of knowing and opens a creative space for radical learning to take place. Novelty stimulates the generative process by which organizations and individuals learn, adapt to and cope with the exigencies they face in order to survive and progress. Such radical learning occurs when creative linguistic interventions in dialogue opens up semantic spaces whereby new terms are coined and old ones broken up, combined and/or redeployed in novel ways, in an effort to give expression to the fresh circumstances experienced or new phenomena observed. We call this kind of imaginative linguistic intervention semantic transformation. In this paper we argue that it is this semantic transformation that promotes radical transformational learning. Such semantic transformation is predicated on the improvisatory character of dialogue as a form of communication. We explore how, through this dialogical process of semantic transformation, we discover the resources and means to respond to the vagueness and equivocality experienced, by exploiting language in novel ways in our attempts to make sense of and account for such experiences.
This paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.
Convolutional Neural Networks (CNNs) have shown to offer a consistent and reliable foundation for the automatic detection of potential exoplanets. CNNs rely on an abundance of parameters (overparameterization) to achieve their impressive detection performances. Astronet was one of the first CNNs for exoplanet detection. It takes as input folded lightcurves in two views: a local view (the transit) and a global view (the entire orbital period including the transit). A more recent CNN called Exonet-XS improved on Astronet’s performance while having considerably less parameters, thereby reducing the risk of overfitting. Exonet-XS also uses two views as input. In this paper, we propose Genesis, an even more simplified CNN for exoplanet detection from folded lightcurves using only one view. In addition, we propose to use a more reliable validation procedure that is custom in CNN-based exoplanet detection studies: the Monte Carlo Cross-Validation (MCCV) procedure. We show that the use of MCCV improves the reliability of the estimation of the detection performance by providing a (discretized) probability distribution, rather than a point estimate. Using MCCV we show that Astronet with only one view performs on a par with the original two-view version. More importantly, our fair comparative evaluation (without stellar parameters and centroids) reveals that Genesis outperforms Exonet-XS and Astronet. We conclude by stating that existing exoplanet detection CNNs are too complex for the task at hand and that future evaluations of performances should use MCCV or similar validation procedures.
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