Preschool children's screen time during the COVID-19 pandemic: associations with family characteristics and children's anxiety/withdrawal and approaches to learning.

With schools closed due to the COVID-19, many children have been exposed to media devices for learning and entertainment, raising concerns over excessive screen time for young children. The current study examined how preschoolers' screen time was associated with their family characteristics and anxiety/withdrawal and approaches to learning during the COVID-19 pandemic. Participants were 764 caregivers of 3- to 6-year-old children (mean age = 59.07 months, SD = 12.28 months; 403 boys and 361 girls) from nine preschools in Wuhan, China, where the pandemic started. The effects of family characteristics on children's screen time during the pandemic outbreak and the associations between screen time and children's anxiety/withdrawal and approaches to learning were examined using path analysis. The results showed that children who spent more time on interactive screen use (e.g., playing with tablets) showed higher levels of anxiety/withdrawal and fewer positive learning behaviors. Unexpectedly, children who spent more time on noninteractive screen use (e.g., watching TV) showed lower levels of anxiety/withdrawal. Additionally, children's screen time was related to family characteristics: children living in more chaotic families with fewer screen time restrictions spent more time on screen use after the pandemic outbreak. The findings suggest that young children's frequent use of interactive screens, such as tablets and smartphones, might be harmful to their learning and wellbeing during the pandemic. To mitigate the potential negative effects, it is essential to manage the screen time of preschoolers by establishing rules for their interactive screen use and improving the household routines related to the overall screen use.

Longitudinal relations between the approximate number system and symbolic number skills in preschool children.

This study examined the longitudinal relation between the approximate number system (ANS) and two symbolic number skills, namely word problem-solving skill and number line skill, in a sample of 138 Chinese 4- to 6-year-old children. The ANS and symbolic number skills were measured first in the second year of preschool (Time 1 [T1], mean age = 4.98 years; SD = 0.33) and then in the third year of preschool (Time 2 [T2]). Cross-lagged analyses indicated that word problem-solving skill at T1 predicted ANS acuity at T2 but not vice versa. In addition, there were bidirectional relations between children's word problem-solving skill and number line estimation skill. The observed longitudinal relations were robust to the control of child's sex, age, maternal education, receptive vocabulary, spatial visualization, and working memory except for the relation between T1 word problem-solving skill and T2 number line estimation skill, which was explained by child's age.

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.

A variety test platform for the standardization and data quality improvement of crop variety tests.

Variety testing is an indispensable and essential step in the process of creating new improved varieties from breeding to adoption. The performance of the varieties can be compared and evaluated based on multi-trait data from multi-location variety tests in multiple years. Although high-throughput phenotypic platforms have been used for observing some specific traits, manual phenotyping is still widely used. The efficient management of large amounts of data is still a significant problem for crop variety testing. This study reports a variety test platform (VTP) that was created to manage the whole workflow for the standardization and data quality improvement of crop variety testing. Through the VTP, the phenotype data of varieties can be integrated and reused based on standardized data elements and datasets. Moreover, the information support and automated functions for the whole testing workflow help users conduct tests efficiently through a series of functions such as test design, data acquisition and processing, and statistical analyses. The VTP has been applied to regional variety tests covering more than seven thousand locations across the whole country, and then a standardized and authoritative phenotypic database covering five crops has been generated. In addition, the VTP can be deployed on either privately or publicly available high-performance computing nodes so that test management and data analysis can be conveniently done using a web-based interface or mobile application. In this way, the system can provide variety test management services to more small and medium-sized breeding organizations, and ensures the mutual independence and security of test data. The application of VTP shows that the platform can make variety testing more efficient and can be used to generate a reliable database suitable for meta-analysis in multi-omics breeding and variety development projects.

Suitability Evaluation of Crop Variety via Graph Neural Network.

With the continuous growth of the global population, insufficient food production has become an urgent problem to be solved in most countries. At present, using artificial intelligence technology to improve suitability between land and crop varieties to increase crop yields has become a consensus among agricultural researchers. However, there are still many problems in existing works, such as limited crop phenotypic data and the poor performance of artificial intelligence models. In this regard, we take maize as an example to collect a large amount of environmental climate and crop phenotypic traits data at multiple experimental sites and construct an extensive dataset. Then, we introduce a graph neural network model to learn crop suitability evaluation and finally achieve a good evaluation effect. The evaluation results of the model can not only provide a reference for expert evaluation but also judge the suitability of the variety to other test trial sites according to the data of the current one, so as to guide future breeding experiments.

Revisiting Grammatical Complexity in L2 Writing via Exploratory Factor Analysis.

Since the 1990s, grammatical complexity has received substantial research attention in applied linguistics (Bulté and Housen, 2014). The representation of grammatical complexity has expanded in L2 writing with the application of diverse measures in empirical studies in the recent three decades (1991-2020). In response to this situation, we found it important to revisit grammatical complexity, and an exploratory factor analysis was applied to explore latent dimensions (i.e., factors) of grammatical complexity in L2 writing. We analyzed Lu's (2011) 14 grammatical complexity measures in the L2 corpus of the British Academic Written English Corpus. We then proposed a four-factor model with "clausal subordination," "phrasal construction", "global length unit" and "others." The four factors generally align with the types of grammatical complexity proposed in Lu (2011), but differences on six measures are also found. Noteworthy points were discussed to interpret the reasons behind the differences. Research implications are provided to show further research directions.

Spatial skills and number skills in preschool children: The moderating role of spatial anxiety.

Spatial ability is a strong and stable predictor of mathematical performance. However, of the three key components of spatial ability, spatial perception and spatial visualization have received less attention than mental rotation in relation to specific mathematical competencies of young children. Even less is known about the role of spatial anxiety in this relationship. This study examined the longitudinal relations of spatial perception and spatial visualization to three number skills (i.e., number line estimation, subitizing, and word problem-solving) among 190 preschool children, and whether these relations varied as a function of spatial anxiety. The results showed that children's spatial perception and spatial visualization skills, measured in the third preschool year (Time 1 [T1]), were positively associated with their word problem-solving six months later (Time 2 [T2]). Children's T1 spatial perception was also positively associated with their T2 subitizing and number line skills. In addition, T1 spatial anxiety moderated the relation between T1 spatial perception and T2 subitizing: the relation between the two was stronger for children with low levels of spatial anxiety than it was for those with moderate or high levels. The findings offer valuable insights into how spatial cognition and affect jointly relate to children's early number skills.

A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy.

Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.

Establishing a Longitudinal Comparable Scale of Chinese Children's Cognitive Development through Calibrated Projection Linking.

In the past decades, the longitudinal approach has been remarkably and increasingly used in the investigations of children's cognitive development. Recently, many researchers have started to realize the importance and necessity of examining measurement invariance for any further longitudinal analysis. However, there are few empirical studies demonstrating how to conduct further analysis when the assumption of measurement invariance of an instrument is violated. The primary purpose of this study is to explore how a newly-developed calibrated projection method can be applied to reduce the impact of lack of parameter invariance in a longitudinal study of preschool children's cognitive development. The sample consisted of 882 children from China who participated in two waves of the cognitive tests when they were 4 and 5 years old. Before this study was conducted, the IRT method was used to examine the measurement invariance of the instrument. The results showed that five items presented difficulty parameter drift and three items presented discrimination/slope parameter drift. In the study, the invariant items were treated as "common items" and calibrated projection linking was used to establish a comparable scale across two time points. Then the linking method was evaluated by three properties: grade-to-grade growth, grade-to-grade variability, and the separation of distributions. The results showed that the grade-to-grade growth across two waves was larger and exhibited a larger effect size; the grade-to-grade variability showed less scale shrinkage, which indicated a smaller measurement error; the separation of distributions showed a larger growth as well.