Rice transcription factor DPF regulates stress-induced biosynthesis of diterpenoid phytoalexins.

Diterpenoid Phytoalexin Factor (DPF) is a key transcription factor involved in diterpenoid phytoalexin (DP) biosynthesis under non-stressed conditions in rice (Oryza sativa L.). Using clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9, DPF knockout rice lines were generated.

Probiotic-derived ferrichrome induces DDIT3-mediated antitumor effects in esophageal cancer cells.

Esophageal cancer, which is common among the elderly, has the poorest prognosis among gastrointestinal cancers. Previously, we demonstrated that ferrichrome, produced by the probiotic , exhibited anti-tumor effects in various gastrointestinal cancers, including colorectal and gastric cancers, with minimal effects on non-cancerous intestinal cells. However, it remains unclear whether ferrichrome exerts anti-tumor effects in esophageal cancer.

A Neural Network-Based Random Access Protocol for Crowded Massive MIMO Systems.

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance.

Probiotic-derived heptelidic acid exerts antitumor effects on extraintestinal melanoma through glyceraldehyde-3-phosphate dehydrogenase activity control.

Background: Several microorganisms inhabit the mammalian gastrointestinal tract and are associated with the pathogenesis of various diseases, including cancer. Recent studies have indicated that several probiotics produce antitumor molecules and inhibit host tumor progression. We demonstrated that heptelidic acid (HA), a sesquiterpene lactone derived from the probiotic Aspergillus oryzae, exerts antitumor effects against pancreatic cancer in vitro and in vivo.

Application of neural network to simulate the behavior of hospitalizations and their costs under the effects of various polluting gases in the city of São Paulo.

This work aims to obtain an artificial neural network to simulate hospitalizations for respiratory diseases influenced by pollutant gaseous such as CO, PM, PM, NO, O, and SO emitted from 2011 to 2017, in the city of São Paulo. The hospitalization costs were also be calculated. MLP and RBF neural networks have been tested by varying the number of neurons in the hidden layer and the type of equation of the output function.