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Biology subjects

Wu, Y.-Z.

Publications and source records attributed to Wu, Y.-Z..

2 recordsLinked to original sources

Fast exploration is coupled with a less choosy but more reactive learning style in a generalist predator

The hypothesis of slow-fast syndromes predicts a correspondence between personality type and learning style; fast explorers would have a more proactive (fast but inflexible) learning style and slow explorers would be more reactive (slow but flexible) learners. Empirical evidence for this personality-cognition coupling remains inconclusive and heavily biased towards birds. Moreover, most studies did not examine the personality-cognition correlation when the cognitive task is discerning food quality, a scenario directly related to energy acquisition that underpins the evolution of slow-fast syndromes. In this study, we examined the exploration-cognition correlation in the context of avoidance learning in an opportunistic predator - the common sun skink Eutropis multifasciata. We quantified exploration tendencies of individuals in an unfamiliar environment and compared foraging behaviours when lizards associated prey colour and quality during the initial learning trials and subsequent reverse learning trials, where the prey colour-taste combinations were switched. We found that fast explorers were less choosy but more reactive foragers, whereas slow explorers exhibited the opposite learning style. Interestingly, there was no evidence for a learning speed-flexibility trade-off. Our findings are in contrast with conventional predictions and suggest that the two types of exploration-cognition coupling could be different viable responses to fast-changing environmental predictability.

animal behavior and cognition↗

PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors

Accurate and comprehensive peptide precursor ions are crucial to tandem mass spectrometry-based peptide identification. An identification engine can greatly benefit from the search space reduction hinted by credible and detailed precursors. Additionally, both the number of identifications and the spectrum explainability can be increased by considering multiple precursors per spectrum. Here, we propose PepPre, which detects precursors by decomposing peaks into multiple isotope clusters using linear programming methods. The detected precursors are scored and ranked, and the high-scoring ones are used for the following peptide identification. PepPre is evaluated both on regular and cross-linked peptides datasets, and compared with 11 methods in this paper. The experimental results show that PepPre achieves 203% more PSM and 68% more peptide identifications than instrument software for regular peptides, and 99% more PSM and 27% more peptide pair identifications for cross-linked peptides, which also outperforms all other evaluated methods. In addition to the increased identification numbers, further credibility evaluation evidence that the identifications are credible. Moreover, by widening the isolation window of data acquisition from 2 Th to 8 Th, the engine is able to identify at least 64% more PSMs with PepPre, demonstrating the potential advantages of large isolation windows. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/540645v1_ufig1.gif" ALT="Figure 1"> View larger version (5K): org.highwire.dtl.DTLVardef@1272a3corg.highwire.dtl.DTLVardef@45e25aorg.highwire.dtl.DTLVardef@f83e0org.highwire.dtl.DTLVardef@9b04e7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗