ABSTRACT
BACKGROUND: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. RESULTS: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. CONCLUSIONS: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
Subject(s)
Artificial Intelligence , Neural Networks, Computer , Phenotype , Plant Roots , PlantsABSTRACT
BACKGROUND: The article addresses the hypothesis that early telephone intervention for psychiatric outpatients starting antidepressant treatment would increase compliance with pharmacological treatment and retention time in the study, and thus allow for a more favourable clinical outcome. METHODS: The study focuses on 131 depressed outpatients who participated in a study aiming to obtain full remission. Patients who benefited from three early structured telephone interventions (n=81) were compared with participants who benefited from the usual care (n=50) with no clinical contact before the first clinical assessment at 2 weeks. RESULTS: The intervention proved to have no significant effect on treatment adherence, attrition rate, exclusion rate for adverse events or improvement of depression severity. It was nevertheless associated with increased retention time in the present study. CONCLUSIONS: These results suggest that motivational phone calls may reinforce adhesion in psychiatric patients and provide early opportunities to adapt treatment to individual needs. CLINICAL IMPLICATION: These results suggest that motivational phone calls may reinforce adhesion in psychiatric patients and provide early opportunities to adapt treatment to individual needs.
