TIANA: transcription factors cooperativity inference analysis with neural attention.

BACKGROUND: Growing evidence suggests that distal regulatory elements are essential for cellular function and states. The sequences within these distal elements, especially motifs for transcription factor binding, provide critical information about the underlying regulatory programs. However, cooperativities between transcription factors that recognize these motifs are nonlinear and multiplexed, rendering traditional modeling methods insufficient to capture the underlying mechanisms. Recent development of attention mechanism, which exhibit superior performance in capturing dependencies across input sequences, makes them well-suited to uncover and decipher intricate dependencies between regulatory elements. RESULT: We present Transcription factors cooperativity Inference Analysis with Neural Attention (TIANA), a deep learning framework that focuses on interpretability. In this study, we demonstrated that TIANA could discover biologically relevant insights into co-occurring pairs of transcription factor motifs. Compared with existing tools, TIANA showed superior interpretability and robust performance in identifying putative transcription factor cooperativities from co-occurring motifs. CONCLUSION: Our results suggest that TIANA can be an effective tool to decipher transcription factor cooperativities from distal sequence data. TIANA can be accessed through: https://github.com/rzzli/TIANA

Pavlovian drug discrimination with bupropion as a feature positive occasion setter: Substitution by methamphetamine and nicotine, but not cocaine

Bupropion can serve as a discriminative stimulus (SD) in an operant drug discrimination task, and a variety of stimulants substitute for the bupropion SD. There are no reports, however, of bupropion functioning as a Pavlovian occasion setter (i.e., feature positive modulator). The present experiment seeks to fill this gap in the literature by training bupropion in rats as a feature positive modulator that disambiguates when a light will be paired with sucrose. Specifically, on bupropion (10 mg/kg IP) sessions, offset of 15-sec cue lights were followed by brief delivery of liquid sucrose; saline sessions were similar except no sucrose was available. Rats readily acquired the discrimination with more conditioned responding to the light on bupropion sessions. Bupropion is approved for use as a smoking cessation aid, and more recently has drawn attention as a potential pharmacotherapy for cocaine and methamphetamine abuse. Accordingly, after discrimination training we tested the ability of cocaine (1 to 10 mg/kg), methamphetamine (0.1 to 1 mg/kg), and nicotine (0.00625 to 0.2 mg/kg) to substitute for the bupropion feature. Nicotine (0.05 mg/kg) and methamphetamine (0.3 mg/kg) substituted fully for bupropion; cocaine did not substitute. These results extend previous research on shared stimulus properties between bupropion and other stimulants to a Pavlovian occasion setting function. Further, this is the first report of nicotine and methamphetamine substitution for bupropion. The overlap in stimulus properties might explain the effectiveness of bupropion as a smoking cessation aid and highlight the possible utility of bupropion for treatment of stimulant use disorder

Evolving web-based test automation into agile business specifications

Usually, test automation scripts for a web application directly mirror the actions that the tester carries out in the browser, but they tend to be verbose and repetitive, making them expensive to maintain and ineffective in an agile setting. Our research has focussed on providing tool-support for business-level, example-based specifications that are mapped to the browser level for automatic verification. We provide refactoring support for the evolution of existing browser-level tests into business-level specifications. As resulting business rule tables may be incomplete, redundant or contradictory, our tool provides feedback on coverage