Time-dependent regulation of respiration is widespread across plant evolution.

Establishing the temperature dependence of respiration is critical for accurate predictions of the global carbon cycle under climate change. Diurnal temperature fluctuations, or changes in substrate availability, lead to variations in leaf respiration. Additionally, recent studies hint that the thermal sensitivity of respiration could be time-dependent. However, the role for endogenous processes, independent from substrate availability, as drivers of temporal changes in the sensitivity of respiration to temperature across phylogenies has not yet been addressed. Here, we examined the diurnal variation in the response of respiration to temperatures (R-T relationship) for different lycophyte, fern, gymnosperm and angiosperm species. We tested whether time-dependent changes in the R-T relationship would impact leaf level respiration modelling. We hypothesized that interactions between endogenous processes, like the circadian clock, and leaf respiration would be independent from changes in substrate availability. Overall, we observed a time-dependent sensitivity in the R-T relationship across phylogenies, independent of temperature, that affected modelling parameters. These results are compatible with circadian gating of respiration, but further studies should analyse the possible involvement of the clock. Our results indicate time-dependent regulation of respiration might be widespread across phylogenies, and that endogenous regulation of respiration is likely affecting leaf-level respiration fluxes.

A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients.

Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.