Healthy older adults show impaired relational learning, but improved transitive expression when inferences are made across pre-experimentally known premise relations. Here, we used the transitivity paradigm to ask whether the organizational structure within schemas facilitates the bridging of relations for novel inference for otherwise healthy older adults who are exhibiting early signs of cognitive decline ("at-risk" older adults), and individuals with single- or multiple-domain amnestic mild cognitive impairment (aMCI). Relational learning was impaired in the two older adult groups, but transitive expression was facilitated by prior semantic knowledge of relations. Prior semantic knowledge did not improve novel inference for aMCI individuals. Schematic scaffolding can successfully support inference in preclinical cognitive decline, but such cognitive support may no longer be useful later in the disease process when dysfunction in neural circuitry may be too severe. The findings encourage future work of semantic knowledge and inference in larger samples of aMCI cases.
Max Plank Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands; Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, 6525 EN Nijmegen, The Netherlands.
Face-to-face communication is not only about 'what' is said but also 'how' it is said, both in speech and bodily signals. Beat gestures are rhythmic hand movements that typically accompany prosodic prominence in conversation. Yet, it is still unclear how beat gestures influence language comprehension.
Glycoconjugates are present on microbial surfaces and play critical roles in modulating interactions with the environment and the host. Extensive research on microbial glycans, including elucidating the structural diversity of the glycan moieties of glycoconjugates and polysaccharides, has been carried out to investigate the function of glycans in modulating the interactions between the host and microbes, to explore their potential applications in the therapeutic targeting of pathogenic species, and in the use as probiotics in gut microbiomes. However, glycan-related information is dispersed across numerous databases and a vast amount of literature, which makes it laborious and time-consuming to identify and gather the relevant information about microbial glycosylation.
High-throughput sequencing has exponentially increased peptide sequences, necessitating a computational method to identify multi-functional therapeutic peptides (MFTP) from their sequences. However, existing computational methods are challenged by class imbalance, particularly in learning effective sequence representations. To address this, we propose PSCFA, a prototypical supervised contrastive learning with a feature augmentation method for MFTP prediction.
Large Language Models (LLM) such as the Generative-Pretrained-Transformer (GPT) and Large-Language-Model-Meta-AI (LLaMA) have attracted much attention. There is strong evidence that these models perform remarkably well in various natural language processing tasks. However, how to leverage them in domain-specific use cases and drive value remains an open question.
Drug repositioning, which involves identifying new therapeutic indications for approved drugs, is pivotal in accelerating drug discovery. Recently, to mitigate the effect of label sparsity on inferring potential drug-disease associations (DDAs), graph contrastive learning (GCL) has emerged as a promising paradigm to supplement high-quality self-supervised signals through designing auxiliary tasks, then transfer shareable knowledge to main task, i.e.
