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Abstract Identification of protein–protein interactions (PPI) is among the critical problems in the domain of bioinformatics. Previous studies have utilized different AI-based models for PPI classification with advances in artificial intelligence (AI) techniques. The input to these models is the features extracted from different sources of protein information, mainly sequence-derived features. In this work, we present an AI-based PPI identification model utilizing a PPI network and protein sequences. The PPI network is represented as a graph where each node is a protein pair, and an edge is defined between two nodes if there exists a common protein between these nodes. Each node in a graph has a feature vector. In this work, we have used the language model to extract feature vectors directly from protein sequences. The feature vectors for protein in pairs are concatenated and used as a node feature vector of a PPI network graph. Finally, we have used the Graph-BERT model to encode the PPI network graph with sequence-based features and learn the hidden representation of the feature vector for each node. The next step involves feeding the learned representations of nodes to the fully connected layer, the output of which is fed into the softmax layer to classify the protein interactions. To assess the efficacy of the proposed PPI model, we have performed experiments on several PPI datasets. The experimental results demonstrate that the proposed approach surpasses the existing PPI works and designed baselines in classifying PPI.

Background: Positive fusional vergence (PFV) is vital in maintaining fusion in critical and continuous near tasks such as reading or performing digital screen tasks. This study investigated how PFV changed under various lighting conditions. Methods: This cross-sectional study recruited 34 participants aged between 21 and 25 years, with best corrected visual acuity (BCVA) 0.0 logMAR and insignificant refractive error. Three different illuminations—low illumination (50 lux), medium lighting (100 lux), and high illumination (150 lux)—were used to examine the ocular parameters PFV (blur, break, and recovery points), contrast sensitivity and pupil diameter. Results: Pupil diameter changed significantly in different room illuminations (p = 0.00). There was no significant difference in contrast sensitivity across the three levels of room illumination (p = 0.368). Mean PFV (SD) (blur) was 14.5 (2.5) in 50 lux, 10.2 (2.2) in 100 lux, and 8.2 (2.1) in 150 lux. Under 50, 100 and 150 lux, respectively, the mean PFV (SD) (break) values were 16.7 (2.4), 13.4 (1.8), and 10.8 (2.2), and the mean PFV (SD) (recovery) values were 13.3 (2.1), 10.7 (2.1), and 7.5 (2.7). With increased illumination levels, PFV blur, break, and recovery values were significantly lower (p < 0.001). Conclusions: PFV values were significantly higher in lower illumination. Clinicians should be aware that room illumination affected the PFV values measured.