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Transcription factor binding site(s) (TFBS) gain and loss (i.e., turnover) is a well-documented feature of cis-regulatory module (CRM) evolution, yet little attention has been paid to the evolutionary force(s) driving this turnover process. The predominant ...
Retroviruses are both powerful evolutionary forces and dangerous threats to genome integrity. As such, they have imposed strong selective pressure on their hosts, notably triggering the emergence of restriction factors, such as TRIM5alpha, that act as pote ...
The determination of transcriptional regulatory networks is key to the understanding of biological systems. However, the experimental determination of transcriptional regulatory networks in the laboratory remains difficult and time-consuming, while current ...
The manual design of adaptive controllers for robotic systems that face unpredictable environmental changes is often challenging. There is thus a growing interest in the development of automatic design tools to assist control engineers. One of the most com ...
The genetic diversity of the world's livestock populations is decreasing, both within and across breeds. A wide variety of factors has contributed to the loss, replacement or genetic dilution of many local breeds. Genetic variability within the more common ...
Background: Scleractinian corals are currently a focus of major interest because of their ecological importance and the uncertain fate of coral reefs in the face of increasing anthropogenic pressure. Despite this, remarkably little is known about the evolu ...
Reliable information is a crucial factor influencing decision-making, and thus fitness in all animals. A common source of information comes from inadvertent cues produced by the behavior of conspecifics. Here we use a system of experimental evolution with ...
Recent comparative genomic and other large-scale bioinformatics studies increasingly have been using gene annotations, functional classifications, and complimentary data from the emerging “-omics” disciplines. Indeed, such analyses have better chances to u ...
In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on a microcontroller. Typically, the network to ...
Advances in sequencing technology are yielding DNA sequence data at an alarming rate – a rate reminiscent of Moore's law. Biologists' abilities to analyze this data, however, have not kept pace. On the other hand, the discrete and mechanical nature of the ...
