Genomic identification and expression analysis of acid invertase (AINV) gene family in Dendrobium officinale Kimura et Migo.

BACKGROUND: Dendrobium officinale Kimura et Migo, a renowned traditional Chinese orchid herb esteemed for its significant horticultural and medicinal value, thrives in adverse habitats and contends with various abiotic or biotic stresses. Acid invertases (AINV) are widely considered enzymes involved in regulating sucrose metabolism and have been revealed to participate in plant responses to environmental stress. Although members of AINV gene family have been identified and characterized in multiple plant genomes, detailed information regarding this gene family and its expression patterns remains unknown in D. officinale, despite their significance in polysaccharide biosynthesis. RESULTS: This study systematically analyzed the D. officinale genome and identified four DoAINV genes, which were classified into two subfamilies based on subcellular prediction and phylogenetic analysis. Comparison of gene structures and conserved motifs in DoAINV genes indicated a high-level conservation during their evolution history. The conserved amino acids and domains of DoAINV proteins were identified as pivotal for their functional roles. Additionally, cis-elements associated with responses to abiotic and biotic stress were found to be the most prevalent motif in all DoAINV genes, indicating their responsiveness to stress. Furthermore, bioinformatics analysis of transcriptome data, validated by quantitative real-time reverse transcription PCR (qRT-PCR), revealed distinct organ-specific expression patterns of DoAINV genes across various tissues and in response to abiotic stress. Examination of soluble sugar content and interaction networks provided insights into stress release and sucrose metabolism. CONCLUSIONS: DoAINV genes are implicated in various activities including growth and development, stress response, and polysaccharide biosynthesis. These findings provide valuable insights into the AINV gene amily of D. officinale and will aid in further elucidating the functions of DoAINV genes.

WAKE-mediated modulation of cVA perception via a hierarchical neuro-endocrine axis in Drosophila male-male courtship behaviour.

The nervous and endocrine systems coordinate with each other to closely influence physiological and behavioural responses in animals. Here we show that WAKE (encoded by wide awake, also known as wake) modulates membrane levels of GABAA receptor Resistance to Dieldrin (Rdl), in insulin-producing cells of adult male Drosophila melanogaster. This results in changes to secretion of insulin-like peptides which is associated with changes in juvenile hormone biosynthesis in the corpus allatum, which in turn leads to a decrease in 20-hydroxyecdysone levels. A reduction in ecdysone signalling changes neural architecture and lowers the perception of the male-specific sex pheromone 11-cis-vaccenyl acetate by odorant receptor 67d olfactory neurons. These finding explain why WAKE-deficient in Drosophila elicits significant male-male courtship behaviour.

A Real-Time Affective Computing Platform Integrated with AI System-on-Chip Design and Multimodal Signal Processing System.

Recently, deep learning algorithms have been used widely in emotion recognition applications. However, it is difficult to detect human emotions in real-time due to constraints imposed by computing power and convergence latency. This paper proposes a real-time affective computing platform that integrates an AI System-on-Chip (SoC) design and multimodal signal processing systems composed of electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG) signals. To extract the emotional features of the EEG, ECG, and PPG signals, we used a short-time Fourier transform (STFT) for the EEG signal and direct extraction using the raw signals for the ECG and PPG signals. The long-term recurrent convolution networks (LRCN) classifier was implemented in an AI SoC design and divided emotions into three classes: happy, angry, and sad. The proposed LRCN classifier reached an average accuracy of 77.41% for cross-subject validation. The platform consists of wearable physiological sensors and multimodal signal processors integrated with the LRCN SoC design. The area of the core and total power consumption of the LRCN chip was 1.13 x 1.14 mm2 and 48.24 mW, respectively. The on-chip training processing time and real-time classification processing time are 5.5 µs and 1.9 µs per sample. The proposed platform displays the classification results of emotion calculation on the graphical user interface (GUI) every one second for real-time emotion monitoring.Clinical relevance- The on-chip training processing time and real-time emotion classification processing time are 5.5 µs and 1.9 µs per sample with EEG, ECG, and PPG signal based on the LRCN model.