Genome-wide identification of CCO gene family in cucumber (Cucumis sativus) and its comparative analysis with A. thaliana.

Carotenoid cleavage oxygenase (CCO) is an enzyme capable of converting carotenoids into volatile, aromatic compounds and it plays an important role in the production of two significant plant hormones, i.e., abscisic acid (ABA) and strigolactone (SL). The cucumber plant genome has not been mined for genomewide identification of the CCO gene family. In the present study, we conducted a comprehensive genome-wide analysis to identify and thoroughly examine the CCO gene family within the genomic sequence of Cucumis sativus L. A Total of 10 CCO genes were identified and mostly localized in the cytoplasm and chloroplast. The CCO gene is divided into seven subfamilies i.e. 3 NCED, 3 CCD, and 1 CCD-like (CCDL) subfamily according to phylogenetic analysis. Cis-regulatory elements (CREs) analysis revealed the elements associated with growth and development as well as reactions to phytohormonal, biotic, and abiotic stress conditions. CCOs were involved in a variety of physiological and metabolic processes, according to Gene Ontology annotation. Additionally, 10 CCO genes were regulated by 84 miRNA. The CsCCO genes had substantial purifying selection acting upon them, according to the synteny block. In addition, RNAseq analysis indicated that CsCCO genes were expressed in response to phloem transportation and treatment of chitosan oligosaccharides. CsCCD7 and CsNCED2 showed the highest gene expression in response to the exogenous application of chitosan oligosaccharides to improve cold stress in cucumbers. We also found that these genes CsCCD4a and CsCCDL-a showed the highest expression in different plant organs with respect to phloem content. The cucumber CCO gene family was the subject of the first genome-wide report in this study, which may help us better understand cucumber CCO proteins and lay the groundwork for the gene family's future cloning and functional investigations.

Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks.

BACKGROUND: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters. METHODS: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect. RESULTS: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993. LIMITATIONS: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample. CONCLUSIONS: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.