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Biology subjects

Jacobs, I.

Publications and source records attributed to Jacobs, I..

2 recordsLinked to original sources

Short-term memory, attentional control and brain size in primates

Brain size variability in primates has been attributed to various domain-specific socio-ecological factors. A recently published large-scale study of short-term memory abilities in 41 primate species [1] did not find any correlations with 11 different proxies of external cognitive demands. Here we found that the interspecific variation in test performance shows correlated evolution with total brain size, with the relationship becoming tighter as species with small sample sizes were successively removed, whereas it was not predicted by the often-used encephalization quotient (EQ). In a subsample, we also found that the sizes of brain region thought to be involved in short-term memory did not predict performance better than did overall brain size. The dependence on brain size suggests that domain-general cognitive processes underlie short-term memory as tested in [1]. These results support the emerging notion that comparative studies of brain size do not generally identify domain-specific cognitive adaptations, but rather reveal varying selection on domain-general cognitive abilities. Finally, because attentional processes beyond short- term memory also affected test performance, we suggest that the delayed response test can be refined.

animal behavior and cognition↗

In silico Antibody-Peptide Epitope prediction for Personalized cancer therapy

The human leukocyte antigen (HLA) system is a complex of genes on chromosome 6 in humans that encodes cell-surface proteins responsible for regulating the immune system. Viral peptides presented to cancer cell surfaces by the HLA trigger the immune system to kill the cells, creating Antibody-peptide epitopes (APE). This study proposes an in-silico approach to identify patient-specific APEs by applying complex networks diagnostics on a novel multiplex data structure as input for a deep learning model. The proposed analytical model identifies patient and tumor-specific APEs with as few as 20 labeled data points. Additionally, the proposed data structure employs complex network theory and other statistical approaches that can better explain and reduce the black box effect of deep learning. The proposed approach achieves an F1-score of 80% and 93% on patients one and two respectively and above 90% on tumor-specific tasks. Additionally, it minimizes the required training time and the number of parameters.

cancer biology↗