A framework for generalized group testing with inhibitors and its potential application in neuroscience

The main goal of group testing with inhibitors (GTI) is to efficiently identify a small number of defective items and inhibitor items in a large set of items. A test on a subset of items is positive if the subset satisfies some specific properties. Inhibitor items cancel the effects of defective items, which often make the outcome of a test containing defective items negative. Different GTI models can be formulated by considering how specific properties have different cancellation effects. This work introduces generalized GTI (GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid item plays the roles of both defectives items and inhibitor items. Since the number of instances of GGTI is large (more than 7 million), we introduce a framework for classifying all types of items non-adaptively, i.e., all tests are designed in advance. We then explain how GGTI can be used to classify neurons in neuroscience. Finally, we show how to realize our proposed scheme in practice

Neural coding strategies and mechanisms of competition

A long running debate has concerned the question of whether neural representations are encoded using a distributed or a local coding scheme. In both schemes individual neurons respond to certain specific patterns of pre-synaptic activity. Hence, rather than being dichotomous, both coding schemes are based on the same representational mechanism. We argue that a population of neurons needs to be capable of learning both local and distributed representations, as appropriate to the task, and should be capable of generating both local and distributed codes in response to different stimuli. Many neural network algorithms, which are often employed as models of cognitive processes, fail to meet all these requirements. In contrast, we present a neural network architecture which enables a single algorithm to efficiently learn, and respond using, both types of coding scheme

DGR mutagenic transposition occurs via hypermutagenic reverse transcription primed by nicked template RNA.

Diversity-generating retroelements (DGRs) are molecular evolution machines that facilitate microbial adaptation to environmental changes. Hypervariation occurs via a mutagenic retrotransposition process from a template repeat (TR) to a variable repeat (VR) that results in adenine-to-random nucleotide conversions. Here we show that reverse transcription of the Bordetella phage DGR is primed by an adenine residue in TR RNA and is dependent on the DGR-encoded reverse transcriptase (bRT) and accessory variability determinant (Avd ), but is VR-independent. We also find that the catalytic center of bRT plays an essential role in site-specific cleavage of TR RNA for cDNA priming. Adenine-specific mutagenesis occurs during reverse transcription and does not involve dUTP incorporation, indicating it results from bRT-catalyzed misincorporation of standard deoxyribonucleotides. In vivo assays show that this hybrid RNA-cDNA molecule is required for mutagenic transposition, revealing a unique mechanism of DNA hypervariation for microbial adaptation

Design and construction of a new Drosophila species, D.synthetica, by synthetic regulatory evolution

Here, I merge the principles of synthetic biology^1,2^ and regulatory evolution^3-11^ to create a new species^12-15^ with a minimal set of known elements. Using preexisting transgenes and recessive mutations of Drosophila melanogaster, a transgenic population arises with small eyes and a different venation pattern that fulfills the criteria of a new species according to Mayr's "Biological Species Concept"^7,10^. The genetic circuit entails the loss of a non-essential transcription factor and the introduction of cryptic enhancers. Subsequent activation of those enhancers causes hybrid lethality. The transition from "transgenic organisms" towards "synthetic species", such as Drosophila synthetica, constitutes a safety mechanism to avoid hybridization with wild type populations and preserve natural biodiversity^16-18^. Drosophila synthetica is the first transgenic organism that cannot hybridize with the original wild type population but remains fertile when crossed with other transgenic animals