SpaGraphCCI: Spatial cell-cell communication inference through GAT-based co-convolutional feature integration.

Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell-cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co-convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low-dimensional space.

Antitumor activity of adenoviral vector containing T42 and 4xT42 peptide gene through inducing apoptosis of tumor cells and suppressing angiogenesis.

The T42 peptide, generated from two active fragments of tumstatin, has been shown to have anti‑tumor activity. The adenoviral vector is the most frequently used vector in research and clinical trials for gene therapy. In the present study, the anti‑tumor activity of the T42 peptide and quadruple T42 (4xT42) peptide adenoviral vectors were elucidated for the first time, to the best of our knowledge.

The dynamics of a stoichiometric plant-herbivore model and its discrete analog.

Stoichiometry-based models brought into sharp focus the importance of the nutritional quality of plant for herbivore-plant dynamics. Plant quality can dramatically affect the growth rate of the herbivores and may even lead to its extinction. These results stem from models continuous in time, which raises the question of how robust they are to time discretization.