Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential.

Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to detect complex patterns inherent in the data. The integration of CNN and transformers' attention mechanisms allows for the extraction of relevant information, leading to accurate predictions of hemolytic potential. The proposed method was trained on three distinct data sets of peptide sequences known as recurrent neural network-hemolytic (RNN-Hem), Hlppredfuse, and Combined. Our computational results demonstrated the superior efficacy of our models compared to existing methods. The proposed approach demonstrated impressive Matthews correlation coefficients of 0.5962, 0.9111, and 0.7788 respectively, indicating its effectiveness in predicting hemolytic activity. With its potential to guide experimental efforts in peptide design and drug development, this method holds great promise for practical applications. Integrating CNNs and transformers proves to be a powerful tool in the fields of bioinformatics and therapeutic research, highlighting their potential to drive advancement in this area.

Bayesian Analysis of Air Emission Violations from Waste Incineration and Coincineration Plants.

Waste incineration and coincineration plants in most European countries have frequently updated their flue gas cleaning systems, surpassing in most cases E.U. air emission standards. At the same time, in most developing countries, cement and other coincineration facilities follow less stringent emission regulations and have a mixed record of protecting air quality. The European Union, the United States, and Canada have established penalties for air emission violations that account for the harm done to the environment and to human health and aiming to remove the economic benefit reaped as a result of noncompliance. Despite their legal completeness, these regulations do not adequately address the probabilistic nature of air pollution. This article recasts the issue of air pollution penalties in a Bayesian decision-making framework with the aspiration that the assessment of penalties on a rigorous mathematical framework can assist in alleviating the mistrust by sections of the public on the effectiveness of air pollution regulations. Integration of economic analyses into risk assessments of emission violations can help clarify to policymakers the effect of environmental policies. Our analysis indicates that the penalty structure of the United States appears to favor the update of emission systems more often than the corresponding European Commission's penalties. Our study advances the use of the loss function as a risk analysis tool that can be used as a public policy instrument to promote environmentally friendlier air emission choices. A parabolic, compared to a linear, loss function was seen to justify higher expenses in gas cleaning systems.