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A transcription factor (TF) is a sequence-specific DNA-binding protein, which plays key roles in cell-fate decision by regulating gene expression. Predicting TFs is key for tea plant research community, as they regulate gene expression, influencing plant growth, development, and stress responses. It is a challenging task through wet lab experimental validation, due to their rarity, as well as the high cost and time requirements. As a result, computational methods are increasingly popular to be chosen. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved impressive performance. In this paper, we present a novel recognition algorithm named TeaTFactor that utilizes pre-training for the model training of TFs prediction. The model is built upon the BERT architecture, initially pre-trained using protein data from UniProt. Subsequently, the model was fine-tuned using the collected TFs data of tea plants. We evaluated four different word segmentation methods and the existing state-of-the-art prediction tools. According to the comprehensive experimental results and a case study, our model is superior to existing models and achieves the goal of accurate identification. In addition, we have developed a web server at http://teatfactor.tlds.cc, which we believe will facilitate future studies on tea transcription factors and advance the field of crop synthetic biology.

Objective: To investigate the effect of enteral nutrition (EN) start time on pH value of gastric juice and ventilator-associated pneumonia (VAP) in critically ill patients with invasive mechanical ventilation, so as to provide reference for the rational selection of EN timing., Methods: Patients with mechanical ventilation who underwent EN treatment admitted to the intensive care unit (ICU) of the First Affiliated Hospital of Zhengzhou University from January 1st, 2016 to November 30th, 2017 were enrolled, and the inclusion criteria were age ≥ 18 years, acute physiology and chronic health evaluation II (APACHE II) ≤ 15, and nutritional risk screening (NRS 2002) ≥ 3. Patients were divided into two groups according to the start time of EN: early EN group (implementation of EN within 48 hours after mechanical ventilation) and late EN group (implementation of EN after 48 hours after mechanical ventilation). The pH value of gastric juice, VAP incidence, mechanical ventilation time, and the length of ICU stay were compared between the two groups., Results: A total of 108 patients were included, 54 in the early EN group and 54 in the late EN group respectively. The pH value of gastric juice in early EN group was lower than that in late EN group [4.8 (3.8, 5.8) vs. 5.6 (4.6, 6.6), P < 0.01]. There were 8 patients with VAP in the early EN group, 3 of whom were early onset VAP. There were 17 patients with VAP in the late EN group, 10 of whom were early onset VAP. The incidence of VAP and the incidence of premature VAP in the early EN group were significantly lower than those in the late EN group (14.8% vs. 31.5%, 5.6% vs. 18.5%, both P < 0.05). The mechanical ventilation time [days: 7.5 (5.7, 9.0) vs. 8.6 (6.8, 10.7) and the length of ICU stay [days: 10.0 (8.5, 11.7) vs. 11.0 (9.5, 12.6)] in the early EN group were significantly shorter than those in the late EN group (all P < 0.05)., Conclusions: At the same time of protecting gastric mucosa, early EN is helpful to reduce the incidence of VAP, reduce the duration of mechanical ventilation and the length of ICU stay, and improve the prognosis.