Cell wall response to UV radiation in needles of Picea omorika

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classification task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classification. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fingerprint with scattered light and laser induced fluorescence. Morphological properties of a particle are sensed with the light scattering process, while chemical properties are encoded with fluorescence spectrum and fluorescence lifetime induced by high-resolution laser. By utilizing these three data modalities, scattering, spectrum, and lifetime, deep learning-based models with millions of parameters are learned to distinguish different pollen classes, but a proper understanding of such a black-box model decisions demands additional methods to employ. Our study provides the first results of applied explainable artificial intelligence (xAI) methodology on the pollen classification model. Extracted knowledge on the important features that attribute to the predicting particular pollen classes is further examined from the perspective of domain knowledge and compared to available reference data on pollen sizes, shape, and laboratory spectrofluorometer measurements.

Effects and Mechanism of Enhanced UV-B Radiation on the Flag Leaf Angle of Rice

Leaf angle is an influential agricultural trait that influences rice (Oryza sativa L.) plant type and yield, which results from the leaf bending from the vertical axis to the abaxial axis. UV-B radiation affects plant morphology, but the effects of varying UV-B intensities on rice flag leaves and the underlying molecular, cellular, and physiological mechanisms remain unknown. This experiment aims to examine the effect of natural light and field-enhanced UV-B radiation (2.5, 5.0, 7.5 kJ·m⁻²) on the leaf angle of the traditional rice variety Baijiaolaojing on Yuanyang terraces. In comparison with natural light, the content of brassinolide and gibberellin in rice flag leaves increased by 29.94% and 60.1%, respectively. The auxin content decreased by 17.3%. Compared with the natural light treatment, the cellulose content in the pulvini was reduced by 13.8% and hemicellulose content by 25.7% under 7.5 kJ·m⁻² radiation intensity. The thick-walled cell area and vascular bundle area of the leaf pulvini decreased with increasing radiation intensity, and the growth of mechanical tissue in the rice leaf pulvini was inhibited. The flag leaf angle of rice was greatest at 7.5 kJ·m⁻² radiation intensity, with an increase of 50.2%. There are two pathways by which the angle of rice flag leaves is controlled under high-intensity UV-B radiation. The leaf angle regulation genes OsBUL1, OsGSR1, and OsARF19 control hormone levels, whereas the ILA1 gene controls fiber levels. Therefore, as cellulose, hemicellulose, sclerenchyma, and vascular bundles weaken the mechanical support of the pulvini, the angle of the flag leaf increases.