Extensive research has been carried out on bacterial secretion systems, as they can pass effector proteins directly into the cytoplasm of host cells.The correct prediction of type IV protein effectors secreted by T4SS is important, since they are known to play a noteworthy role in various human pathogens.Studies on predicting T4SS effectors involve
One of the central puzzles in the study of sociocultural evolution is how and why transitions from small-scale human groups to large-scale, hierarchically more complex ones occurred.Here we develop a spatially explicit agent-based model as a first step towards understanding garmin 67 the ecological dynamics of small and large-scale human groups.By
Purpose: This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images.Materials and Methods: We classified 1,332 stones into 31 classes according to the stone composition.The top 4 classes with a frequency of 110 or more (class 1: calcium oxalate monohydrate [COM] 100%, class 2: COM 80%+struvite 20%, cla