A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions.

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

Extreme fire weather is the major driver of severe bushfires in southeast Australia.

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019-2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires.

Risk factors of systematic biliary complications in patients with gallbladder stones.

BACKGROUND: Gallbladder stone with symptom is an indication of cholecystectomy according to the current guideline. However, about 80% of gallstone patients are asymptomatic. The identification of gallbladder stone patients likely to develop symptomatic complications would be of benefit in clinical practice. AIMS: The aim of this study was to investigate the risk factors for asymptomatic gallstone diseases developing into a gallstone-related biliary event. METHODS: This retrospective study evaluated 1284 patients with gallstones and received laparoscopic cholecystectomy (LC) in Peking Union Medical College Hospital. The age-gender-matched patients were divided into the acute cholecystitis group (group A), biliary colic group (group B), and asymptomatic gallstone group (group C). Baseline clinical characteristics and serum biochemical indexes were recorded and analyzed among the groups. Univariate logistic regression and multivariate Cox proportional hazard regression analysis were performed to determine the risk factors. RESULTS: The incidence of diabetes, hypertension, and sludge was higher in group A than in group B and in group C. The concentrations of plasma HDL in group A were lower than those in group B and group C (P < 0.05, 1.12 ± 0.19, vs. 1.21 ± 0.22, vs. 1.21 ± 0.21). Logistic regression analysis showed that diabetes (OR = 1.39, 95% CI 1.07-1.56, P = 0.028), sludge (OR = 1.09, 95% CI 1.01-1.18, P = 0.022), HDL (OR = 0.85, 95% CI 0.70-1.02, P = 0.045), and FBG (1.91, 95% CI 1.26-2.90, P = 0.034) were significantly associated with biliary events. CONCLUSIONS: Diabetes, sludge, and lower blood HDL level are risk factors for symptomatic gallstone diseases.