Effects of different light intensity on the growth of tomato seedlings in a plant factory.

Light-emitting diodes (LEDs) were the best artificial light source for plant factories. Red light-emitting diodes (LEDs, R) and blue light-emitting diodes (LEDs, B) were used to obtain different light intensities of uniform spectra, and the greenhouse environment was considered as a comparison. The results showed that root dry weight, shoot dry weight and stem diameter were superior in plant growth under 240 µmolm-2s-1, additionally, the Dixon Quality Index (DQI) was also best. Under 240 µmolm-2s-1, the net photosynthesis rate (Pn) was consistent with the greenhouse's treatment, superior to other experimental groups. The results implied that the PPFD was more suitable for the cultivation of tomato seedlings under the condition of 240 µmolm-2s-1, and can replace the greenhouse conditions so as to save energy and reduce emissions.

Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study.

Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features associated with functional deficits. Here, we proposed an improved model of Graph Isomorphism Network (GIN) that implements the Weisfeiler-Lehman (WL) graph isomorphism test to learn the graph features while taking into account the importance of each node in the classification to improve the interpretability of the algorithm. We applied the proposed method on multisite datasets of resting-state functional connectome from Autism Brain Imaging Data Exchange (ABIDE) after stringent quality control. The proposed method outperformed other commonly used classification methods on five different evaluation metrics. We also identified salient ROIs in visual and frontoparietal control networks, which could provide potential neuroimaging biomarkers for ASD identification.