12(S)-hydroxyeicosatetraenoic acid (12-HETE) is one of the metabolites of arachidonic acid involved in pathological conditions associated with mitochondria and oxidative stress. The present study tested effects of 12-HETE on mitochondrial functions. In isolated rat heart mitochondria, 12-HETE increases intramitochondrial ionized calcium concentration that stimulates mitochondrial nitric oxide (NO) synthase (mtNOS) activity. mtNOS-derived NO causes mitochondrial dysfunctions by decreasing mitochondrial respiration and transmembrane potential. mtNOS-derived NO also produces peroxynitrite that induces release of cytochrome c and stimulates aggregation of mitochondria. Similarly, in HL-1 cardiac myocytes, 12-HETE increases intramitochondrial calcium and mitochondrial NO, and induces apoptosis. The present study suggests a novel mechanism for 12-HETE toxicity.

Machine learning progress relies on algorithm benchmarks. We study the problem of declaring a winner, or ranking "candidate" algorithms, based on results obtained by "judges" (scores on various tasks). Inspired by social science and game theory on fair elections, we compare various ranking functions, ranging from simple score averaging to Condorcet methods. We devise novel empirical criteria to assess the quality of ranking functions, including the generalization to new tasks and the stability under judge or candidate perturbation. We conduct an empirical comparison on the results of 5 competitions and benchmarks (one artificially generated). While prior theoretical analyses indicate that no single ranking function satisfies all desired properties, our empirical study reveals that the classical "average rank" method fares well. However, some pairwise comparison methods can get better empirical results.

The application of artificial neural network approaches has been successful in solving complex civil engineering problems, such as damage detection and structural member capacity prediction. Within the context of the present study, corrosion has become the main factor limiting the safety and load-carrying capacity of aging steel bridge girders. Corrosion damage is often most severe near girder ends in simple-span bridges due to deck joint leakage and the pooling of water and de-icing salts. In addition to empirical methods, Finite Element (FE) analysis is typically used to evaluate the residual bearing capacity of corroded steel girders. However, it is prohibitively challenging and time-consuming to create an accurate FE model of a corroded girder due to the irregular nature of corrosion damage. Resultantly, corrosion damage is often reduced to uniform section loss, which leads to unreliable estimates of a girder’s residual bearing capacity. Researchers have proposed methodologies for modeling irregular corrosion damage, but these approaches require a high level of expertise. A comprehensive method is therefore required to efficiently estimate the residual bearing capacity of a corroded steel girder. This paper proposes the use of neural networks to predict the residual bearing capacity of corroded steel plate models as a first step in estimating the residual bearing capacity of an in-service girder. Neural networks are constructed and trained on a database built from FE analysis performed on steel plate models with realistic representations of corrosion damage. This study assesses the ability of neural networks to estimate the compressive capacity of corroded steel plates since plate girders are one of the most prevalent girder forms in steel bridges. Three types of neural networks are trained to predict the compressive capacity of corroded plate models, including a multilayer perceptron (MLP), a convolutional neural network (CNN), and a hybrid MLP-CNN model. The average mean absolute percentage errors (MAPE) for the three models are 20.65%, 11.46%, and 9.64%, respectively. The results of this study demonstrate the potential of using neural networks to predict the compressive capacity of corroded plates efficiently and accurately, which could facilitate proactive maintenance decision-making for aging bridges.