Bliss, L. | Search bioRxiv

bioRxiv2022

Newborn chicks prefer stimuli that move against gravity

At the beginning of life, inexperienced animals use evolutionary-given preferences (predispositions) to decide what stimuli attend and approach. Stimuli that contain cues of animacy, such as face-like stimuli, biological motion and changes in speed, are particularly attractive across vertebrate taxa. A strong cue of animacy is upward movement against terrestrial gravity, because only animate objects consistently move upward. To test whether upward movement is spontaneously considered attractive already at birth, we tested the early preferences of dark-hatched chicks (Gallus gallus) for upward vs downward moving visual stimuli. We found that, without any previous visual experience, chicks consistently exhibited a preference to approach upward moving stimuli, that move against gravity. A control experiment showed that these preferences are not driven by avoidance of downward stimuli. These results show that newborn animals are spontaneously attracted by upward movement, indicating that movement against gravity can be used as a cue of animacy to orient early approach responses in the absence of previous visual experience.

animal behavior and cognition↗

bioRxiv2019

Hermes: an ensemble machine learning architecture for protein secondary structure prediction

MotivationProtein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on ten existing pipelines providing rapid, high accuracy, 3-State secondary structure prediction of proteins.\n\nResultsAfter training on 1348 solved protein structures, we evaluated the model with four independent datasets: JPRED4 - compiled by the authors of the successful predictor with the same name, and CASP11, CASP12 & CASP13 - assembled by the Critical Assessment of protein Structure Prediction consortium who run biannual experiments focused on objective testing of predictors. These rigorous, pre-established protocols included 7-fold cross-validation and blind testing. This led to a mean Hermes accuracy of 95.5%, significantly (p<0.05) better than the ten previously published models analysed in this paper. Furthermore, Hermes yielded a reduction in standard deviation, lower boundary outliers, and reduced dependency on solved structures of homologous proteins, as measured by NEFF score. This architecture provides advantages over other pipelines, while remaining accessible to users at any level of bioinformatics experience.\n\nAvailability and ImplementationThe source code for Hermes is freely available at: https://github.com/HermesPrediction/Hermes. This page also includes the cross-validation with corresponding models, and all training/testing data presented in this study with predictions and accuracy.

bioinformatics↗