Fine root morphology and growth in response to nitrogen addition through drip fertigation in a Populus × euramericana “Guariento” plantation over multiple years

International audienceAbstractKey messageNitrogen addition through drip fertigation to a poplar plantation (Populus × euramericana“Guariento”) promoted fine root growth only in the early period. The relationship between root growth and soil N content was positive in the first 2 years, but became negative in the third year when the soil N availability had substantially increased.ContextNitrogen (N) deficiency is common in forest soils, and N addition is sometimes applied in the case of intensive plantations. There is a need to better document the impact of N addition through the high-efficiency fertilization technique on fine root morphology and growth, given their importance for the uptake of nutrients and for tree growth.AimsWe aimed to quantitatively investigate the responses of fine roots in morphology and growth to N addition through surface drip fertigation over multiple years in a Populus × euramericana “Guariento” plantation.MethodsA field experiment that included four drip fertigation treatments with N addition levels (0, 60, 120, and 180 kg N ha−1 year−1) was conducted for three successive years. A coring method was used to sample soils and quantify the root morphological traits and soil N content along 0–60-cm profiles.ResultsThe root biomass density, length, surface area, specific length, and tissue density were significantly higher in the N addition treatments than those in the control after the first year, but the positive effect decreased in the second year. In the third year, root biomass in the N addition treatments was even lower by 11–39% than that in the control. The relationship between root growth and soil N content was also positive in the first 2 years and negative in the third year.ConclusionN addition promoted fine root growth mainly in the shallow soil and in the early period of experiment. The relationship between root growth and soil N content became negative in the third year when the soil N availability had substantially increased. It is suggested that fine roots adjust their growth and morphology in response to N availability varying along the soil profile and with the fertilization duration

Construction of a Fish-like Robot Based on High Performance Graphene/PVDF Bimorph Actuation Materials.

Smart actuators have many potential applications in various areas, so the development of novel actuation materials, with facile fabricating methods and excellent performances, are still urgent needs. In this work, a novel electromechanical bimorph actuator constituted by a graphene layer and a PVDF layer, is fabricated through a simple yet versatile solution approach. The bimorph actuator can deflect toward the graphene side under electrical stimulus, due to the differences in coefficient of thermal expansion between the two layers and the converse piezoelectric effect and electrostrictive property of the PVDF layer. Under low voltage stimulus, the actuator (length: 20 mm, width: 3 mm) can generate large actuation motion with a maximum deflection of about 14.0 mm within 0.262 s and produce high actuation stress (more than 312.7 MPa/g). The bimorph actuator also can display reversible swing behavior with long cycle life under high frequencies. on this basis, a fish-like robot that can swim at the speed of 5.02 mm/s is designed and demonstrated. The designed graphene-PVDF bimorph actuator exhibits the overall novel performance compared with many other electromechanical avtuators, and may contribute to the practical actuation applications of graphene-based materials at a macro scale

GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.Comment: AAAI 2019; change the template and fix some typo