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Benavides, R.

Publications and source records attributed to Benavides, R..

2 recordsLinked to original sources

Intraspecific and intraindividual trait variability decrease with tree species richness in a subtropical tree biodiversity experiment

Phenotypic variability within tree species responds to local tree species richness. However, we lack evidence on how different sources of trait variation shape tree-tree interactions. Along a diversity gradient from one to eight tree species, we sampled 4,568 leaves from 381 trees to study changes in intraspecific and intraindividual leaf trait variability and assessed their contribution to community functional diversity. Intraspecific variability decreased with tree species richness, while intraindividual variability barely responded. Functional overlap between conspecific trees increased with tree species richness and through intraindividual variation, but was reduced through intraspecific variability, meaning that intraspecific variability may reduce intraspecific competitive interactions while intraindividual variability could arise due to varying microenvironmental conditions within the canopy. Last, intraspecific and intraindividual variability explained high community functional richness and divergence, respectively, especially in mixtures. Our findings emphasize that fine-scale variability influences tree-tree interactions and can be a driver of local functional diversity. TeaserTrait variability within tree species decreases with diversity and explains high forest functional diversity.

ecology↗

A graph-embedded topic model enables characterization of diverse pain phenotypes among UK Biobank individuals

Large biobank repositories of clinical conditions and medications data open opportunities to investigate the phenotypic disease network. To enable systematic investigation of entire structured phenomes, we present graph embedded topic model (GETM). Our contributions are two folds in terms of method and applications. On the methodology side, we offer two main contributions in GETM. First, to aid topic inference, we integrate existing biomedical knowledge graph information in the form of pre-trained graph embedding into the embedded topic model. Second, leveraging deep learning techniques, we developed a variational autoencoder framework to infer patient phenotypic mixture by modeling multi-modal discrete patient medical records. In particular, for interpretability, we use a linear decoder to simultaneously infer the bi-modal distributions of the disease conditions and medications. On the application side, we applied GETM to UK Biobank (UKB) self-reported clinical phenotype data, which contains 443 self-reported medical conditions and 802 self-reported medications for 457,461 individuals. Compared to existing methods, GETM demonstrates overall superior performance in imputing missing conditions and medications. Here, we focused on characterizing pain phenotypes recorded in the questionnaire of the UKB individuals. GETM accurately predicts the status of chronic musculoskeletal (CMK) pain, chronic pain by body-site, and non-specific chronic pain using past conditions and medications. Our analyses revealed not only the known pain-related topics but also the surprising predominance of medications and conditions in the cardiovascular category among the most predictive topics across chronic pain phenotypes.

bioinformatics↗