Many Gram-negative bacteria infect hosts and cause diseases by translocating a variety of type III secreted effectors (T3SEs) into the host cell cytoplasm. However, despite a dramatic increase in the number of available whole-genome sequences, it remains challenging for accurate prediction of T3SEs. Traditional prediction models have focused on atypical sequence features buried in the N-terminal peptides of T3SEs, but unfortunately, these models have had high false-positive rates. In this research, we integrated promoter information along with characteristic protein features for signal regions, chaperone-binding domains, and effector domains for T3SE prediction. Machine learning algorithms, including deep learning, were adopted to predict the atypical features mainly buried in signal sequences of T3SEs, followed by development of a voting-based ensemble model integrating the individual prediction results. We assembled this into a unified T3SE prediction pipeline, T3SEpp, which integrated the results of individual modules, resulting in high accuracy (i.e., ∼0.94) and >1-fold reduction in the false-positive rate compared to that of state-of-the-art software tools. The T3SEpp pipeline and sequence features observed here will facilitate the accurate identification of new T3SEs, with numerous benefits for future studies on host-pathogen interactions. IMPORTANCE Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.
As a virtual digital model that can reflect physical entities or systems, digital twins are revolutionizing industry. The first prerequisite for the construction of digital twins is the establishment of high-precision and complex entities or system models. A 47-components numerical system is established for the core engine test rig main test system by using the finite volume modularization modeling method. A comprehensive solution to the system-level valve-spool/orifice throttling modeling, the key issue of the fluid pipeline system modeling, is presented, and the algorithms of throttling and mixing are deepened and expanded. The full-process simulation study on two tests of normal-temperature 1400 s and low-temperature 1240 s shows that the combined regulation of five regulator valves and the change of cold source directly decide dynamic change of the system in each stage; the simulation reveals the phenomena such as the gas cylinder cooling with deflation, the air cooling when expanding from main pipeline to two branch pipelines, shunting flow by branch pipeline, and the cold and hot gases mixing; the overall variation trends of the simulation curves are consistent with those of all the experimental curves of the test rig normal-temperature/low-temperature air supply lines, exhaust bypass, and engine main line in two operating conditions, and the maximum error between simulation curves and test curves of pressure, total pressure, and total temperature is less than 12%. The numerical system can be used for the construction of virtual models of digital twins, and the modeling method provides a feasible solution to the key technology of digital twins.
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