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Abstract Background Golden leaf in autumn is a prominent feature of deciduous tree species like Ginkgo biloba L., a landscape tree widely cultivated worldwide. However, little was known about the molecular mechanisms of leaf yellowing, especially its dynamic regulatory network. Here, we performed a suite of comparative physiological and dynamic transcriptional analyses on the golden-leaf cultivar and the wild type (WT) ginkgo to investigate the underlying mechanisms of leaf yellowing across different seasons. Results In the present study, we used the natural bud mutant cultivar with yellow leaves “Wannianjin” (YL) as materials. Physiological analysis revealed that higher ratios of chlorophyll a to chlorophyll b and carotenoid to chlorophyll b caused the leaf yellowing of YL. On the other hand, dynamic transcriptome analyses showed that genes related to chlorophyll metabolism played key a role in leaf coloration. Genes encoding non-yellow coloring 1 (NYC1), NYC1-like (NOL), and chlorophyllase (CLH) involved in the degradation of chlorophyll were up-regulated in spring. At the summer stage, down-regulated HEMA encoding glutamyl-tRNA reductase functioned in chlorophyll biosynthesis, while CLH involved in chlorophyll degradation was up-regulated, causing a lower chlorophyll accumulation. In carotenoid metabolism, genes encoding zeaxanthin epoxidase (ZEP) and 9-cis-epoxy carotenoid dioxygenase (NCED) showed significantly different expression levels in the WT and YL. Moreover, the weighted gene co-expression network analysis (WGCNA) suggested that the most associated transcriptional factor, which belongs to the AP2/ERF-ERF family, was engaged in regulating pigment metabolism. Furthermore, quantitative experiments validated the above results. Conclusions By comparing the golden-leaf cultivar and the wide type of ginkgo across three seasons, this study not only confirm the vital role of chlorophyll in leaf coloration of YL but also provided new insights into the seasonal transcriptome landscape and co-expression network. Our novel results pinpoint candidate genes for further wet-bench experiments in tree species.

Anxiety induction is widely used in the investigations of the mechanism and treatment of state anxiety. State anxiety is accompanied by immediate psychological and physiological responses. However, the existing state anxiety measurement, such as the commonly used state anxiety subscale of the State-Trait Anxiety Inventory, mainly relies on questionnaires with low temporal resolution. This study aims to develop a tracking model of state anxiety with high temporal resolution. To capture the dynamic changes of state anxiety levels, we induced the participants' state anxiety through exposure to aversive pictures or the risk of electric shocks and simultaneously recorded multi-modal data, including dimensional emotion ratings, electrocardiogram, and galvanic skin response. Using the paired self-reported state anxiety levels and multi-modal measures, we trained and validated machine learning models to predict state anxiety based on psychological and physiological features extracted from the multi-modal data. The prediction model achieved a high correlation between the predicted and self-reported state anxiety levels. This quantitative model provides fine-grained and sensitive measures of state anxiety levels for future affective brain-computer interaction and anxiety modulation studies.

Affect recognition is an interdisciplinary research field bringing together researchers from natural and social sciences. Affect recognition research aims to detect the affective state of a person based on observables, with the goal to, for example, provide reasoning for the person’s decision making or to support mental wellbeing (e.g., stress monitoring). Recently, beside of approaches based on audio, visual or text information, solutions relying on wearable sensors as observables, recording mainly physiological and inertial parameters, have received increasing attention. Wearable systems enable an ideal platform for long-term affect recognition applications due to their rich functionality and form factor, while providing valuable insights during everyday life through integrated sensors. However, existing literature surveys lack a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods and best practices of wearable affect and stress recognition. Following a summary of different psychological models, we detail the influence of affective states on the human physiology and the sensors commonly employed to measure physiological changes. Then, we outline lab protocols eliciting affective states and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain and review common approaches related to the preprocessing, feature extraction and classification steps. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field to conduct and evaluate user studies and develop wearable systems.