Artificial neural networks (ANN) are computationally based mathematical tools inspired by the fundamental cell of the nervous system, the neuron. ANN constitute a simplified artificial replica of the human brain consisting of parallel processing neural elements similar to neurons in living beings. ANN is able to store large amounts of experimental information to be used for generalization with the aid of an appropriate prediction model. ANN has proved useful for a variety of biological, medical, economic and meteorological purposes, and in agro-food science and technology. The olive oil industry has a substantial weight in Mediterranean's economy. The different steps of the olive oil production process, which include olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in depth with a view to their optimization, and so have the authenticity, sensory properties and other quality-related properties of olive oil. This paper reviews existing literature on the use of bioinformatics predictive methods based on ANN in connection with the production, processing and characterization of olive oil. It examines the state of the art in bioinformatics tools for optimizing or predicting its quality with a view to identifying potential deficiencies or aspects for improvement.
Chemical fungicides can cause drug resistance of plant pathogenic fungi, environmental pollution, and potential threats to humans and animals. Therefore, developing low-toxicity, high-efficient and environment-friendly biological control products is critical for green prevention, controlling plant fungal diseases, and maintaining ecological balance. Biocontrol research mainly includes the following aspects: antagonistic microorganisms, fungicidal proteins, RNA interference techniques and botanical fungicides. Significantly, natural products extracted from medicinal plants are valuable repertoire for inhibiting plant fungal diseases. This review systematically reviewed the research advances of using natural products from medicinal plants to inhibit plant pathogenic fungi, including the types of natural products, extraction methods, and antifungal mechanisms. The further prospects for the study and application, which provide the reference for botanical fungicide development and practical application in preventing and controlling plant fungal disease, were also discussed.
Objectives: Target organ damage and cardiovascular risk are more closely associated with ambulatory (ABPM) than with clinic blood pressure (BP). Moreover, it has been suggested that nighttime BP is a better predictor of risk than daytime or 24 h BP. All previous trials on the prognostic value of ABPM have relied only on a single baseline profile from each participant, without accounting for changes in BP during follow-up. We have compared to prognostic value of ABPM and clinic BP in the MAPEC study, where participants were systematically evaluated by repeated ABPM. Methods: This prospective study investigated 3344 subjects (1718 men), 52.6 ± 14.5 years of age. At baseline, BP was measured every 20-min from 07:00 to 23:00 h and every 30-min at night for 48 h. Just before starting ABPM, six clinic BP measurements were obtained with a validated oscillometric device. ABPM was scheduled yearly or more frequently (quarterly) if treatment adjustment was required. The Cox proportional-hazard model was used to estimate relative risks of cardiovascular events associated to clinic and ambulatory BP. Results: After a median follow-up time of 5.6 years, ambulatory BP predicted outcome better than clinic BP. Moreover, the asleep BP mean was consistently a stronger predictor than the awake or 24 h BP mean. Based on the baseline ABPM, with adjustment for gender, age, diabetes, antihypertensive treatment, and clinic BP, the relative hazard ratio (HR) for each 10-mmHg increase in systolic BP was 1.18 (1.09–1.28; P < 0.001) for awake mean and 1.29 (1.21–1.37; P < 0.001) for asleep mean. Cardiovascular morbidity was even more strongly associated with systolic asleep BP when data were analyzed on the basis of the ABPM profile closer to the event (1.30, 1.23–1.38, P < 0.001). Conclusions: ABPM is superior to clinic BP in predicting cardiovascular morbidity and mortality. After adjustment for relevant confounding risk factors, the asleep BP mean provides higher prognostic value than awake BP mean. Results further indicate that decreasing asleep BP reduces cardiovascular risk beyond 24h-mean BP lowering.
