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Jasmonic acid (JA) and salicylic acid (SA) phytohormone pathways are important regulators of stress tolerance. Knowledge regarding the diversity, phylogeny and functionality of wheat genes involved in JA and SA response is limited.Using Arabidopsis, rice and wheat genomic and wheat disease transcriptomic data, we deduced the size, phylogenetic diversity and pathogen‐responsiveness of seven hormone‐responsive gene families, and thus selected 14 candidates as potential hormone responsive gene markers. Gene‐specific expression studies assessed the impact of exogenous JA and SA on their transcriptional activation in leaves of two distinct wheat cultivars. RNAseq data were interrogated to assess their disease responsiveness and tissue‐specific expression.This study elucidated the number, phylogeny and pathogen‐responsiveness of wheat genes from seven families, including 12 TaAOS, 6 TaJAMyb, 256 TaWRKY group III, 85 TaPR1, 205 TaPR2, 76 TaPR3 and 124 TaPR5. This included the first description of the wheat AOS, JAMyb, PR2, PR3 and PR5 gene families. Gene expression studies delineated TaAOS1‐5B and TaJAMyb‐4A as JA‐responsive in leaves, but not significantly responsive to SA treatment, while TaWRKY45‐B was a SA‐ but not a JA‐responsive marker. Other candidate genes were either unresponsive or non‐specific to SA or JA.Our findings highlight that all seven gene families are greatly expanded in wheat as compared to other plants (up to 7.6‐fold expansion), and demonstrate disparity in the response to biotic stress between some homoeologous and paralogous sequences within these families. The SA‐ and JA‐responsive marker genes identified herein will prove useful tools to monitor these signalling pathways in wheat. [ABSTRACT FROM AUTHOR]

Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism‐related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver‐report and clinician administered measures of autism‐related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA‐based gaze variables related to social attention were associated with the level of autism‐related behaviors. Two language‐related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism‐related behaviors, and measure changes in autism‐related behaviors over time. Lay Summary: This study explored whether behaviors measured by a computer are related to clinician‐administered and caregiver‐reported ratings of behavior. An iOS application (app) called SenseToKnow was created to elicit a wide range of autism‐related behaviors using strategically designed movies and games. The app recorded videos of the child while they watched the movies and their touch data and computer vision analysis measured behaviors including the child's attention to social stimuli, such as people and speech, and fine motor control. We found that several behaviors digitally measured via the app are related to commonly used and standardized caregiver‐report and clinician‐administered measures of social, language, and adaptive behaviors. These results suggest that the digital app may be of clinical value in identifying specific clinical profiles associated with autism. Future research is needed to determine the usefulness of the app as an autism screening tool and for monitoring changes in behavior over time. [ABSTRACT FROM AUTHOR]

Background: Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. Methods: Data were collected from 457 toddlers, 17–36 months old, during their well‐child visit to four pediatric primary care clinics. Forty‐one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front‐facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. Results: Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. Conclusions: Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism. [ABSTRACT FROM AUTHOR]

Background: This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call. Methods: During a pediatric primary care well‐child visit, 910 toddlers, 17–37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty‐seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human. Results: CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD. Conclusions: A digital app delivered on an iPhone or iPad in real‐world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom—failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms. [ABSTRACT FROM AUTHOR]

Summary: Taxonomically‐restricted orphan genes play an important role in environmental adaptation, as recently demonstrated by the fact that the Pooideae‐specific orphan TaFROG (Triticum aestivum Fusarium Resistance Orphan Gene) enhanced wheat resistance to the economically devastating Fusarium head blight (FHB) disease. Like most orphan genes, little is known about the cellular function of the encoded protein TaFROG, other than it interacts with the central stress regulator TaSnRK1α. Here, we functionally characterized a wheat (T. aestivum) NAC–like transcription factor TaNACL‐D1 that interacts with TaFROG and investigated its' role in FHB using studies to assess motif analyses, yeast transactivation, protein‐protein interaction, gene expression and the disease response of wheat lines overexpressing TaNACL‐D1. TaNACL‐D1 is a Poaceae‐divergent NAC transcription factor that encodes a Triticeae‐specific protein C‐terminal region with transcriptional activity and a nuclear localisation signal. The TaNACL‐D1/TaFROG interaction was detected in yeast and confirmed in planta, within the nucleus. Analysis of multi‐protein interactions indicated that TaFROG could form simultaneously distinct protein complexes with TaNACL‐D1 and TaSnRK1α in planta. TaNACL‐D1 and TaFROG are co‐expressed as an early response to both the causal fungal agent of FHB, Fusarium graminearum and its virulence factor deoxynivalenol (DON). Wheat lines overexpressing TaNACL‐D1 were more resistant to FHB disease than wild type plants. Thus, we conclude that the orphan protein TaFROG interacts with TaNACL‐D1, a NAC transcription factor that forms part of the disease response evolved within the Triticeae. [ABSTRACT FROM AUTHOR]