Effects of task structure and confirmation bias in alternative hypotheses evaluation.

We empirically examined the effectiveness of how the Analysis of Competing Hypotheses (ACH) technique structures task information to help reduce confirmation bias (Study 1) and the portrayal of intelligence analysts as suffering from such bias (Study 2). Study 1 (N = 161) showed that individuals presented with hypotheses in rows and evidence items in columns were significantly less likely to demonstrate confirmation bias, whereas those presented with the ACH-style matrix (with hypotheses in columns and evidence items in rows) or a paragraph of text (listing the evidence for each hypothesis) were not less likely to demonstrate bias. The ACH-style matrix also did not confer any benefits regarding increasing sensitivity to evidence credibility.

Refining the transcriptional landscapes for distinct clades of virulent phages infecting .

The introduction of high-throughput sequencing has resulted in a surge of available bacteriophage genomes, unveiling their tremendous genomic diversity. However, our current understanding of the complex transcriptional mechanisms that dictate their gene expression during infection is limited to a handful of model phages. Here, we applied ONT-cappable-seq to reveal the transcriptional architecture of six different clades of virulent phages infecting .

Phage proteins target and co-opt host ribosomes immediately upon infection.

Bacteriophages must seize control of the host gene expression machinery to replicate. To bypass bacterial anti-phage defence systems, this host takeover occurs immediately upon infection. A general understanding of phage mechanisms for immediate targeting of host transcription and translation processes is lacking.

Exploring the transcriptional landscape of phage-host interactions using novel high-throughput approaches.

In the last decade, powerful high-throughput sequencing approaches have emerged to analyse microbial transcriptomes at a global scale. However, to date, applications of these approaches to microbial viruses such as phages remain scarce. Tailoring these techniques to virus-infected bacteria promises to obtain a detailed picture of the underexplored RNA biology and molecular processes during infection.

SAPPHIRE.CNN: Implementation of dRNA-seq-driven, species-specific promoter prediction using convolutional neural networks.

Data availability is a consistent bottleneck for the development of bacterial species-specific promoter prediction software. In this work we leverage genome-wide promoter datasets generated with dRNA-seq in the Gram-negative bacteria and for promoter prediction. Convolutional neural networks are presented as an optimal architecture for model training and are further modified and tailored for promoter prediction.