Systemically modeling the relationship between climate change and wheat aphid abundance

Terpene Synthase Gene OtLIS Confers Wheat Resistance to Sitobion avenae by Regulating Linalool Emission

Sitobion avenae (Fabricius) is a major insect pest of wheat worldwide that reduces crop yield and quality annually. Few germplasm resources with resistant genes to aphids have been identified and characterized. Here, octoploid Trititrigia, a species used in wheat distant hybridization breeding, was found to be repellent to S. avenae after 2 year field investigations and associated with physiological and behavioral assays. Linalool monoterpene was identified to accumulate dominantly in plants in response to S. avenae infestation. We cloned the resistance gene OtLIS by assembling the transcriptome of aphid-infested or healthy octoploid Trititrigia. Functional characterization analysis indicated that OtLIS encoded a terpene synthase and conferred resistance to S. avenae by linalool emission before and after aphid feeding. Our study suggests that the octoploid Trititrigia with the aphid-resistant gene OtLIS may have potential as a target resource for further breeding aphid-resistant wheat cultivars.

Artificial intelligence deciphers codes for color and odor perceptions based on large-scale chemoinformatic data

BACKGROUND

Color vision is the ability to detect, distinguish, and analyze the wavelength distributions of light independent of the total intensity. It mediates the interaction between an organism and its environment from multiple important aspects. However, the physicochemical basis of color coding has not been explored completely, and how color perception is integrated with other sensory input, typically odor, is unclear.

RESULTS

Here, we developed an artificial intelligence platform to train algorithms for distinguishing color and odor based on the large-scale physicochemical features of 1,267 and 598 structurally diverse molecules, respectively. The predictive accuracies achieved using the random forest and deep belief network for the prediction of color were 100% and 95.23% ± 0.40% (mean ± SD), respectively. The predictive accuracies achieved using the random forest and deep belief network for the prediction of odor were 93.40% ± 0.31% and 94.75% ± 0.44% (mean ± SD), respectively. Twenty-four physicochemical features were sufficient for the accurate prediction of color, while 39 physicochemical features were sufficient for the accurate prediction of odor. A positive correlation between the color-coding and odor-coding properties of the molecules was predicted. A group of descriptors was found to interlink prominently in color and odor perceptions.

CONCLUSIONS

Our random forest model and deep belief network accurately predicted the colors and odors of structurally diverse molecules. These findings extend our understanding of the molecular and structural basis of color vision and reveal the interrelationship between color and odor perceptions in nature.

Causal Inference of Optimal Control Water Level and Inflow in Reservoir Optimal Operation Using Fuzzy Cognitive Map

Reservoir optimal operation (ROO) has always been a hot issue in the field of water resources management. Analysis of the relationship of optimal control water level and inflow is conducive to understanding and solving ROO under deterministic inflow conditions. The current research uses a fuzzy cognitive map (FCM) as a tool to effectively model complex systems and then extracts systematic relationship diagrams from the dataset. A new fuzzy cognitive map with offset (FCM-O) is proposed to overcome the causal inference error caused by non-linear mapping of the activation function in a traditional FCM. With the application of inferring the causal relationship between the optimal control water level and inflow of ROO for the Three Gorges Reservoir (TGR), the experimental results show that, compared with FCM in the min data error, FCM-O reduces 11.11% and 7.14% in the training and the testing, respectively. Also, the experimental results of FCM-O are more reasonable than those of FCM. Finally, the following conclusions about the causal inference of optimal control water level and inflow in ROO for TGR are drawn: (1) The optimal control water level in September, October and November needs to be raised as much as possible to raise the water head of power generation, which is mainly affected by the constraints of the maximum operating water level of the reservoir rather than inflow; (2) the optimal control water level in January, February and March is positively affected by the inflow of the adjacent months; (3) the optimal control water level in April is due to the approaching flood season. In order to prevent water discarding, the water level is low and the optimum operation space is small. All of those shows that FCM-O is more competent than FCM in the causal relationship between optimal control water level and inflow in ROO.