Synthases

Novel synthases and the corresponding nucleic acids encoding such synthases are disclosed herein. Such synthases possess an active site pocket that includes key amino acid residues that are modified to generate desired terpenoid reaction intermediates and products. Synthase modifications are designed based on, e.g., the three-dimensional coordinates of tobacco 5-epi-aristolochene synthase with or without a substrate bound in the active site

Methods of Making Modified Polypeptides

Novel polypeptides and the corresponding nucleic acids encoding such polypeptides are disclosed herein. The invention provides methods of making modified polypeptides by altering one or more amino acid residues involved in the active site of a preselected polypeptide

Synthases

Novel synthases and the corresponding nucleic acids encoding such synthases are disclosed herein. Such synthases possess an active site pocket that includes key amino acid residues that are modified to generate desired terpenoid reaction intermediates and products. Synthase modifications are designed based on, e.g., the three-dimensional coordinates of tobacco 5-epi-aristolochene synthase. with or without a substrate bound in the active site

Synthases

Novel synthases and the corresponding nucleic acids encoding such synthases are disclosed herein. Such synthases possess an active site pocket that includes key amino acid residues that are modified to generate desired terpenoid reaction intermediates and products. Synthase modifications are designed based on, e.g., the three-dimensional coordinates of tobacco 5-epi-aristolochene synthase, with or without a substrate bound in the active site

Synthases

Novel synthases and the corresponding nucleic acids encoding such synthases are disclosed herein. Such synthases possess an active site pocket that includes key amino acid residues that are modified to generate desired terpenoid reaction intermediates and products. Synthase modifications are designed based on, e.g., the three-dimensional coordinates of tobacco 5-epi-aristolochene synthase, with or without a substrate bound in the active site

Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch

Developing effective learning systems for Machine Learning (ML) applications in the Neuromorphic (NM) field requires extensive experimentation and simulation. Software frameworks aid and ease this process by providing a set of ready-to-use tools that researchers can leverage. The recent interest in NM technology has seen the development of several new frameworks that do this, and that add up to the panorama of already existing libraries that belong to neuroscience fields. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications. We emphasize the availability of spiking neuron models and learning rules to more easily direct decisions on the most suitable frameworks to carry out different types of research. Furthermore, we present an extension to the SpykeTorch framework that gives users access to a much broader choice of neuron models to embed in SNNs and make the code publicly available

Enhanced Light Extraction from OLEDs Fabricated on Patterned Plastic Substrates

A key scientific and technological challenge in organic light-emitting diodes (OLEDs) is enhancing the light outcoupling factor ηout, which is typically \u3c20%. This paper reports experimental and modeling results of a promising approach to strongly increase ηout by fabricating OLEDs on novel flexible nanopatterned substrates that result in a \u3e2× enhancement in green phosphorescent OLEDs (PhOLEDs) fabricated on corrugated polycarbonate (PC). The external quantum efficiency (EQE) reaches 50% (meaning ηout ≥50%); it increases 2.6x relative to a glass/ITO device and 2× relative to devices on glass/poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) or flat PC/PEDOT:PSS. A significant enhancement is also observed for blue PhOLEDs with EQE 1.7× relative to flat PC. The corrugated PC substrates are fabricated efficiently and cost-effectively by direct room-temperature molding. These substrates successfully reduce photon losses due to trapping/waveguiding in the organic+anode layers and possibly substrate, and losses to plasmons at the metal cathode. Focused ion beam gauged the conformality of the OLEDs. Dome-shaped convex nanopatterns with height of ∼280–400 nm and pitch ∼750–800 nm were found to be optimal. Substrate design and layer thickness simulations, reported first for patterned devices, agree with the experimental results that present a promising method to mitigate photon loss paths in OLEDs

Simple and complex spiking neurons : perspectives and analysis in a simple STDP scenario

Spiking neural networks (SNNs) are largely inspired by biology and neuroscience, and leverage ideas and theories to create fast and efficient learning systems. Spiking neuron models are adopted as core processing units in neuromorphic systems because they enable event-based processing. The integrate-and-fire (I\&F) models are often adopted as considered more suitable, with the simple Leaky I\&F (LIF) being the most used. The reason for adopting such models is their efficiency or biological plausibility. Nevertheless, rigorous justification for the adoption of LIF over other neuron models for use in artificial learning systems has not yet been studied. This work considers a variety of neuron models in the literature and then selects computational neuron models that are single-variable, efficient, and display different types of complexities. From this selection, we make a comparative study of three simple I\&F neuron models, namely the LIF, the Quadratic I\&F (QIF) and the Exponential I\&F (EIF), to understand whether the use of more complex models increases the performance of the system and whether the choice of a neuron model can be directed by the task to be completed. Neuron models are tested within an SNN trained with Spike-Timing Dependent Plasticity (STDP) on a classification task on the N-MNIST and DVS Gestures datasets. Experimental results reveal that more complex neurons manifest the same ability as simpler ones to achieve high levels of accuracy on a simple dataset (N-MNIST), albeit requiring comparably more hyper-parameter tuning. However, when the data possess richer spatio-temporal features, the QIF and EIF neuron models steadily achieve better results. This suggests that accurately selecting the model based on the richness of the feature spectrum of the data could improve the performance of the whole system. Finally, the code implementing the spiking neurons in the SpykeTorch framework is made publicly available