Search

Abstract Differences in social attention are well-documented in autistic individuals, representing one of the earliest signs of autism. Spontaneous blink rate has been used to index attentional engagement, with lower blink rates reflecting increased engagement. We evaluated novel methods using computer vision analysis (CVA) for automatically quantifying patterns of attentional engagement in young autistic children, based on facial orientation and blink rate, which were captured via mobile devices. Participants were 474 children (17–36 months old), 43 of whom were diagnosed with autism. Movies containing social or nonsocial content were presented via an iPad app, and simultaneously, the device’s camera recorded the children’s behavior while they watched the movies. CVA was used to extract the duration of time the child oriented towards the screen and their blink rate as indices of attentional engagement. Overall, autistic children spent less time facing the screen and had a higher mean blink rate compared to neurotypical children. Neurotypical children faced the screen more often and blinked at a lower rate during the social movies compared to the nonsocial movies. In contrast, autistic children faced the screen less often during social movies than during nonsocial movies and showed no differential blink rate to social versus nonsocial movies.

Fusarium fungi produce a diverse array of mycotoxic metabolites during the pathogenesis of cereals. Some, such as the trichothecenes and fumonisins, are phytotoxic, acting as non-proteinaceous effectors that facilitate disease development in cereals. Over the last few decades, we have gained some depth of understanding as to how trichothecenes and fumonisins interact with plant cells and how plants deploy mycotoxin detoxification and resistance strategies to defend themselves against the producer fungi. The cereal-mycotoxin interaction is part of a co-evolutionary dance between Fusarium and cereals, as evidenced by a trichothecene-responsive, taxonomically restricted, cereal gene competing with a fungal effector protein and enhancing tolerance to the trichothecene and resistance to DON-producing F. graminearum. But the binary fungal–plant interaction is part of a bigger ecosystem wherein other microbes and insects have been shown to interact with fungal mycotoxins, directly or indirectly through host plants. We are only beginning to unravel the extent to which trichothecenes, fumonisins and other mycotoxins play a role in fungal-ecosystem interactions. We now have tools to determine how, when and where mycotoxins impact and are impacted by the microbiome and microfauna. As more mycotoxins are described, research into their individual and synergistic toxicity and their interactions with the crop ecosystem will give insights into how we can holistically breed for and cultivate healthy crops.

Abstract Increasing evidence suggests that early motor impairments are a common feature of autism. Thus, scalable, quantitative methods for measuring motor behavior in young autistic children are needed. This work presents an engaging and scalable assessment of visual-motor abilities based on a bubble-popping game administered on a tablet. Participants are 233 children ranging from 1.5 to 10 years of age (147 neurotypical children and 86 children diagnosed with autism spectrum disorder [autistic], of which 32 are also diagnosed with co-occurring attention-deficit/hyperactivity disorder [autistic+ADHD]). Computer vision analyses are used to extract several game-based touch features, which are compared across autistic, autistic+ADHD, and neurotypical participants. Results show that younger (1.5-3 years) autistic children pop the bubbles at a lower rate, and their ability to touch the bubble’s center is less accurate compared to neurotypical children. When they pop a bubble, their finger lingers for a longer period, and they show more variability in their performance. In older children (3-10-years), consistent with previous research, the presence of co-occurring ADHD is associated with greater motor impairment, reflected in lower accuracy and more variable performance. Several motor features are correlated with standardized assessments of fine motor and cognitive abilities, as evaluated by an independent clinical assessment. These results highlight the potential of touch-based games as an efficient and scalable approach for assessing children’s visual-motor skills, which can be part of a broader screening tool for identifying early signs associated with autism.

The wheat NAC transcription factor TaNACL-D1 enhances resistance to the economically devastating Fusarium head blight (FHB) disease. The objective of this study was to decipher the alterations in gene expression, pathways and biological processes that led to enhanced resistance as a result of the constitutive expression of TaNACL-D1 in wheat. Transcriptomic analysis was used to determine the genes and processes enhanced in wheat due to TaNACL-D1 overexpression, both in the presence and absence of the causal agent of FHB, Fusarium graminearum (0- and 1-day post-treatment). The overexpression of TaNACL-D1 resulted in more pronounced transcriptional reprogramming as a response to fungal infection, leading to the enhanced expression of genes involved in detoxification, immune responses, secondary metabolism, hormone biosynthesis, and signalling. The regulation and response to JA and ABA were differentially regulated between the OE and the WT. Furthermore, the results suggest that the OE may more efficiently: (i) regulate the oxidative burst; (ii) modulate cell death; and (iii) induce both the phenylpropanoid pathway and lignin synthesis. Thus, this study provides insights into the mode of action and downstream target pathways for this novel NAC transcription factor, further validating its potential as a gene to enhance FHB resistance in wheat.

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]

Atypical facial expression is one of the early symptoms of autism spectrum disorder (ASD) characterized by reduced regularity and lack of coordination of facial movements. Automatic quantification of these behaviors can offer novel biomarkers for screening, diagnosis, and treatment monitoring of ASD. In this work, 40 toddlers with ASD and 396 typically developing toddlers were shown developmentally-appropriate and engaging movies presented on a smart tablet during a well-child pediatric visit. The movies consisted of social and non-social dynamic scenes designed to evoke certain behavioral and affective responses. The front-facing camera of the tablet was used to capture the toddlers’ face. Facial landmarks’ dynamics were then automatically computed using computer vision algorithms. Subsequently, the complexity of the landmarks’ dynamics was estimated for the eyebrows and mouth regions using multiscale entropy. Compared to typically developing toddlers, toddlers with ASD showed higher complexity (i.e., less predictability) in these landmarks’ dynamics. This complexity in facial dynamics contained novel information not captured by traditional facial affect analyses. These results suggest that computer vision analysis of facial landmark movements is a promising approach for detecting and quantifying early behavioral symptoms associated with ASD. [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]