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Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Reciprocal association between theory of mind and reading comprehension of narrative (but not expository) text in middle childhood: A latent change score approach
(Elsevier Inc, 2026-01-01) Gao Q; Xu T; Chen P; Zhang R; Wang Z
Abstract This study presents a longitudinal evidence of co-occurring developmental changes in theory of mind (ToM) and reading comprehension in a group of 159 children (ages 8–10; M = 9.96, SD = 0.93; 92 girls). We tracked participants over one year using identical measures of ToM, narrative reading comprehension (NRC), and expository reading comprehension (ERC) at two time points. Applying a Latent Change Score (LCS) model, we found that individual differences in ToM and NRC not only influenced each other's growth over time but were also significantly correlated at both initial measurement and in their change scores. However, only initial ToM was associated with gains in ERC during the one-year interval, but not vice versa. These findings suggest a reciprocal causal relationship between socio-cognitive and academic development and highlight the importance of integrating both domains in educational interventions. Educational relevance statement Our findings demonstrate that Theory of Mind (ToM) and narrative reading comprehension (NRC) are reciprocally related over time, suggesting that strengthening one domain can accelerate growth in the other. Importantly, children with stronger initial abilities in either ToM or NRC experienced greater gains in the other domain, indicating the risk or widening achievement gaps without early support. Moreover, ToM predicted later gains in expository reading comprehension (ERC), underscoring its role in supporting comprehension of increasingly complex academic texts. These results suggest that integrating ToM and reading comprehension training within educational practice can enhance cognitive and academic development in tandem. Such integration may be particularly impactful for students at risk of early learning difficulties, offering a promising direction for targeted, developmentally informed interventions. Preregistration: https://doi.org/10.17605/OSF.IO/69Q5R Data: https://data.mendeley.com/datasets/zfzd852xpg/1
Earlier false belief understanding predicts later lie-telling behavior in preschool children, but not vice versa
(John Wiley and Sons Ltd, 2024-11) Wang Z; Gao X; Shao Y
Young children's lie-telling behavior is associated with their theory of mind (ToM) development. However, current evidence is primarily based on cross-sectional studies, with very little longitudinal evidence on the causal relation between the two constructs. The current study provided much-needed cross-lagged longitudinal evidence on the association between ToM and lying in young children. Adopting a short-term longitudinal design, we tested 104 normally developing children's (64 boys, M = 54.0 months) false belief understanding and lie-telling behaviors three times at 4-month intervals. Results showed the cross-lagged model fit the data well. Lie-telling behaviors exhibited moderate stability across the three time points, while ToM exhibited moderate stability between the first two time points but not between Time 2 and Time 3. Earlier false belief understanding significantly predicted children's later lie-telling behavior, controlling for family socioeconomic status, child age, gender, only child status, and Time 1 verbal ability and inhibitory control. On the contrary, earlier lie-telling did not predict later false beliefs understanding. We concluded that earlier false belief understanding predicts later lie-telling behavior in preschool children, but not vice versa.
Advanced theory of mind and children's prosocial lie-telling in middle childhood: A training study
(Elsevier Inc., 2024-10-01) Gao Q; Chen P; Huang Q; Wang Z
Children's advanced theory of mind (AToM) is concurrently associated with their prosocial lie-telling. However, the causal link between AToM and prosocial lie-telling has not yet been demonstrated. To address this gap, the current study adopted a training paradigm and investigated the role of AToM in children's prosocial lie-telling in middle childhood. A total of 66 9- and 10-year-old children who did not demonstrate any prosocial lie-telling in a disappointment gift paradigm at the baseline were recruited and randomly assigned to either the experimental group (n = 32) or an active control group (n = 34). The experimental group underwent a conversation-based training program of four sessions. The results showed significantly greater gains in AToM at the posttest for the experimental group children compared with the control group children, controlling for family socioeconomic status, children's literacy score, working memory, and inhibition. More important, the experimental group children were more likely to tell prosocial lies than the control group, even after controlling for the pretest AToM and other covariates. However, the training effects faded at the 6-month follow-up test after the training's completion. These findings provide the first evidence for the causal role of AToM in the development of prosocial lie-telling in middle childhood. The fade-out effect is discussed in the context of educational interventions.
The Application of Artificial Intelligence and Big Data in the Food Industry
(MDPI (Basel, Switzerland), 2023-12-18) Ding H; Tian J; Yu W; Wilson DI; Young BR; Cui X; Xin X; Wang Z; Li W; Yılmaz MT
Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.
Comparative transcriptomes reveal geographic differences in the ability of the liver of plateau zokors (Eospalax baileyi) to respond and adapt to toxic plants
(BioMed Central Ltd, 2023-12) Tan Y; Wang Y; Liu Q; Wang Z; Shi S; Su J
BACKGROUND: Environmental changes are expected to intensify in the future. The invasion of toxic plants under environmental changes may change herbivore feeding environments. Herbivores living long-term in toxic plant-feeding environments will inevitably ingest plant secondary metabolites (PSMs), and under different feeding environments are likely to have unique protection mechanisms that support improved adaptation to PSMs in their habitat. We aimed to compare different subterranean herbivore population responses and adaptations to toxic plants to unveil their feeding challenges. RESULTS: Here, we investigated the adaptive capacity of the liver in two geographically separated populations of plateau zokors (Eospalax baileyi) before and after exposure to the toxic plant Stellera chamaejasme (SC), at the organ, biochemical, and transcriptomic levels. The results showed no significant liver granules or inflammatory reactions in the Tianzhu (TZ) population after the SC treatment. The transaminase level in the TZ population was significantly lower than that in the Luqu population. Transcriptome analysis revealed that the TZ population exhibited interactions with other detoxification metabolic pathways by oxytocin pathway-associated genes, including diacylglycerol lipase alpha (Dagla), calcium/calmodulin dependent protein kinase II Alpha (Camk2a), and CD38 molecule (Cd38). The phase II process of liver drug metabolism increased to promote the rate of metabolism. We found that alternative splicing (AS) and the expression of the cyclin D (Ccnd1) gene interact-a TZ population hallmark-reduced liver inflammatory responses. CONCLUSION: Our study supports the detoxification limitation hypothesis that differences in liver detoxification metabolism gene expression and AS are potential factors in herbivore adaptation to PSMs and may be a strategy of different herbivore populations to improve toxic plant adaptability.
Learning and integration of adaptive hybrid graph structures for multivariate time series forecasting
(Elsevier Inc., 2023-11-01) Guo T; Hou F; Pang Y; Jia X; Wang Z; Wang R
Recent status-of-the-art methods for multivariate time series forecasting can be categorized into graph-based approach and global-local approach. The former approach uses graphs to represent the dependencies among variables and apply graph neural networks to the forecasting problem. The latter approach decomposes the matrix of multivariate time series into global components and local components to capture the shared information across variables. However, both approaches cannot capture the propagation delay of the dependencies among individual variables of a multivariate time series, for example, the congestion at intersection A has delayed effects on the neighboring intersection B. In addition, graph-based forecasting methods cannot capture the shared global tendency across the variables of a multivariate time series; and global-local forecasting methods cannot reflect the nonlinear inter-dependencies among variables of a multivariate time series. In this paper, we propose to combine the advantages of both approaches by integrating Adaptive Global-Local Graph Structure Learning with Gated Recurrent Units (AGLG-GRU). We learn a global graph to represent the shared information across variables. And we learn dynamic local graphs to capture the local randomness and nonlinear dependencies among variables. We apply diffusion convolution and graph convolution operations to global and dynamic local graphs to integrate the information of graphs and update gated recurrent unit for multivariate time series forecasting. The experimental results on seven representative real-world datasets demonstrate that our approach outperforms various existing methods.
DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network
(BioMed Central Ltd, 2023-09-18) Zhang J; Liu B; Wu J; Wang Z; Li J
Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.
Himalayan Marmot (Marmota himalayana) Redistribution to High Latitudes under Climate Change
(MDPI (Basel, Switzerland), 2023-08-28) Wang Z; Kang Y; Wang Y; Tan Y; Yao B; An K; Su J; Crowther M
Climate warming and human activities impact the expansion and contraction of species distribution. The Himalayan marmot (Marmota himalayana) is a unique mammal and an ecosystem engineer in the Qinghai-Tibet Plateau (QTP). This pest aggravates grassland degradation and is a carrier and transmitter of plagues. Therefore, exploring the future distribution of Himalayan marmots based on climate change and human activities is crucial for ecosystem management, biodiversity conservation, and public health safety. Here, a maximum entropy model was explored to forecast changes in the distribution and centroid migration of the Himalayan marmot in the 2050s and 2070s. The results implied that the human footprint index (72.80%) and altitude (16.40%) were the crucial environmental factors affecting the potential distribution of Himalayan marmots, with moderately covered grassland being the preferred habitat of the Himalayan marmot. Over the next 30-50 years, the area of suitable habitat for the Himalayan marmot will increase slightly and the distribution center will shift towards higher latitudes in the northeastern part of the plateau. These results demonstrate the influence of climate change on Himalayan marmots and provide a theoretical reference for ecological management and plague monitoring.
Soil Microbial Community Composition and Diversity Are Insusceptible to Nitrogen Addition in a Semi-Arid Grassland in Northwestern China
(MDPI (Basel, Switzerland), 2023-10-11) Tuo H; Li M; Ghanizadeh H; Huang J; Yang M; Wang Z; Wang Y; Tian H; Ye F; Li W; Monokrousos N
Human-caused nitrogen (N) deposition is a global environmental issue that can change community composition, functions, and ecosystem services. N deposition affects plants, soil, and microorganisms regionally and is linked to ecosystem, soil, and climate factors. We examined the effects of six N addition levels (0, 2.34 g, 4.67, 9.34,18.68, and 37.35 g N m−2 yr−1) on aboveground vegetation, surface soil properties, and microbial community. Alterations in microbial communities in response to N addition were monitored using 16S rRNA (16S ribosomal ribonucleic acid, where S donates a sedimentation coefficient) and ITS (internal transcribed spacer) regions for bacterial and fungal communities, respectively. N addition positively affected aboveground vegetation traits, such as biomass and community weighted mean of leaf nitrogen. N addition also limited phosphorus (P) availability and altered the microbial community assembly process from random processes to deterministic processes. The microbial community diversity and composition, however, were not sensitive to N addition. Partial least squares structural equation models showed that the composition of bacterial communities was mainly driven by the composition of plant communities and total nitrogen, while the composition of fungal communities was driven by soil pH and community weighted mean of leaf nitrogen. Taken together, the results of this research improved our understanding of the response of grassland ecosystems to N deposition and provided a theoretical basis for grassland utilization and management under N deposition.
Grazing activity increases decomposition of yak dung and litter in an alpine meadow on the Qinghai-Tibet plateau
(Springer Nature Switzerland AG on behalf of the Royal Netherlands Society of Agricultural Science, 2019-11) Yang C; Zhang Y; Hou F; Millner JP; Wang Z; Chang S; Shang Z
Aims: This study investigated the influences of herbivore grazing intensity and grazing season on decomposition and nutrient release of dung and litter, which aimed to improve our understandings of grazing affecting nutrient cycling in alpine meadows on the Qinghai-Tibetan Platean. Methods: A factorial design experiment comprising 3 grazing intensities (non-grazing, moderate grazing, and heavy grazing) and 2 grazing seasons (summer and winter), was applied to quantify the decomposition and chemistry of dung and litter in an alpine pasture using the litterbag technique. Litterbags were retrieved for analysis of mass loss and nutrient release with 180, 360, 540, and 720 days after placement. Results: Grazing activity accelerated the decomposition of dung and litter and increased nutrient release from dung and litter by increasing soil temperature compared with non-grazing pastures, whereas grazing season had no effect on decomposition. The decomposition time was shorter for dung than that for litter. Conclusions: Herbivores grazing benefited dung and litter decomposition and nutrient cycling directly by increasing soil temperature, which is likely to promote soil microbial activity due to low temperatures in alpine meadows, and indirectly through herbage ingestion and dung deposition which increase the organic debris concentration used for microorganisms growth and reproduction. This study provides insights into the mechanisms of grazing regulating nutrient cycling in alpine ecosystems.

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