Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil.

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.

Monitoring underwater explosions in the habitat of resident bottlenose dolphins.

Maintenance work on the harbor of Setúbal, in Portugal, required the removal of a 14-m deep rocky outcrop at the ship maneuver area, using about 35 kg of Gelamonite, a nitroglycerin-based high-explosive. This important harbor is located in the Sado estuary, a biologically rich environment and an important feeding area for a resident community of bottlenose dolphins. Using different safe range calculation models, a mitigation and monitoring plan was developed that minimized the risks of these underwater explosions for the dolphins. At our monitoring station, at 2 km from the demolition site, acoustic pressure levels in excess of 170 dB re 1 µPa (root-mean-square) were measured. Samples of dead fish collected at the site were indicative of shock trauma from the blasts.