Chan-Tat Ng

This study investigated speech obstacles of Taiwanese Mandarin-speaking late talkers at 24 months of age by analyzing their use of gestures independently or alongside speech to resolve communicative challenges, compared to age-matched typically developing children. Spontaneous gesture and speech data were collected during mother–child interactions. Children’s linguistic abilities were also assessed using the Taiwan version of the MacArthur-Bates Communicative Development Inventories. Late talkers showed limited vocabularies, reduced grammatical complexity, and predominantly one-word utterances. However, their gestural abilities were intact, with gesture rates and types comparable to those of their typically developing peers. Late talkers compensated for speech difficulties by relying more on gestures to achieve communicative goals non-verbally or combined with speech. Although they produced fewer cross-modal combinations, these combinations enriched the speech content and allowed communication of more complex ideas about entities and predication. Notably, these cross-modal patterns were not realized entirely in their spoken language, with some patterns lacking verbal equivalents.

A stretchable, high-k dielectric material based on thiol-ene-cross-linked nitrile-butadiene rubber (NBR) for synaptic transistors is demonstrated. We investigated NBR formulations cross-linked with three thiol cross-linkers. The thiol-ene-cross-linked NBR dielectrics achieve a high dielectric constant (k = 14.6), enabling low-voltage transistor operation (2 V^-1 s^-1), a high ON/OFF ratio (10⁴), and a small threshold voltage (0.2 ± 0.4 V). More importantly, these devices effectively mimic synaptic functions. A large hysteresis, driven by dielectric polarization and enhanced by thiol introduction, was observed, particularly pronounced in NBR dielectric with multiple thiol-cross-linkers. The thiol-ene-cross-linked NBR device displayed superior short-term plasticity and long-term potentiation/depression, indicating its learning and memory capabilities. Encouragingly, the fully stretchable NBR transistor maintained good electrical performance, stable hysteresis, and essential synaptic behaviors even at 60% strain. As a practical demonstration for neuromorphic applications, the thiol-ene-cross-linked NBR device exhibited excellent acoustic classification performance, achieving recognition accuracy close to 99% even under mechanical deformation. In summary, the developed thiol-ene cross-linked NBR offers highly promising electronic properties for stretchable, low-voltage neuromorphic devices.

Word problems are essential for math learning and education, bridging numerical knowledge with real‐world applications. Despite their importance, the neural mechanisms underlying word problem solving, especially in children, remain poorly understood. Here, we examine children's cognitive and brain response profiles for arithmetic word problems (AWPs), which involve one‐step mathematical operations, and compare them with nonarithmetic word problems (NWPs), structured as parallel narratives without numerical operations. Behavioural results suggested that AWP performance was associated with both reading comprehension and arithmetic fluency, whereas NWP performance correlated only with reading comprehension. Neuroimaging results revealed distinct neural substrates: AWP solving primarily activated the anterior insula, middle frontal gyrus and intraparietal sulcus, whereas NWP solving engaged in the inferior frontal gyrus, middle temporal gyrus and angular gyrus. Critically, we observed a developmental shift: Children showed heightened prefrontal activation during AWP solving, contrasting with increased posterior parietal engagement in adults. Moreover, although adults demonstrated brain–behaviour associations, with slower AWP solving linked to stronger parietal activation, this relationship was absent in children. Taken together, these findings suggest that AWP solving recruits specialized arithmetic brain circuits that undergo a frontal‐to‐parietal trajectory. Our study thus provides a neurological basis for AWP solving in children, emphasizing the crucial role of the fronto‐insular‐parietal network. These insights into brain‐based contributions to developmental differences may guide the development of targeted remediation strategies and educational interventions tailored to individual learning needs.

In this work, we describe a solid-state polymer electrolyte (SPE)-based electrolyte-gated organic field-effect transistors (EGOFETs) consisting of a thiol-ene-assisted photo-cross-linked nitrile butadiene rubber (NBR) network embedded with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) electrolyte. The photocurable SPE film can be patterned with different dimensions by photolithography and exhibits excellent electronic properties and crucial synaptic behavior. The photocurable NBR/LiTFSI EGOFET exhibits a high transconductance of 11.9 mS and a high on/off ratio of 10⁵ at a scan rate of 40 mV/s. Due to the strongly polarized nature of the photo-cross-linked NBR network and Li-ion diffusion, the NBR/LiTFSI device exhibits a significant current hysteresis, enabling synaptic-like learning and memory behavior. The NBR/LiTFSI device demonstrates a DNN of 91.9% handwritten digit recognition accuracy. This work demonstrates the potential of the solid-state NBR/LiTFSI EGOFET in creating highly efficient and low-energy neuromorphic devices.

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task‐based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7–30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U‐shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN‐SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross‐validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth.Practitioner Points: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task‐state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.

Introduction: Math anxiety severely impacts individuals’ learning and future success. However, limited is understood about the profile in East Asian cultures where students genuinely show high‐level math anxiety, despite that they outperform their Western counterparts. Here, we investigate the relation between math anxiety and math achievement in children as young as first and second graders in Taiwan. Further, we evaluate whether intensive exposure to digital game‐based learning in mathematics could ameliorate math anxiety.Methods: The study first evaluated a group of 159 first and second graders’ math anxiety and its correlation with math performance. Subsequently, a quasi‐experimental design was adopted: 77 of the children continued and participated in multi‐component digital game training targeting enumeration, speeded calculation, and working memory. Post‐assessment was administered afterward for further evaluation of training‐associated effects.Results: Results confirmed that math anxiety was negatively associated with school math achievement, which assessed numerical knowledge and arithmetic calculation. Furthermore, children's math anxiety was remarkably reduced via digital training in mathematics after 6‐week intensive remediation. Crucially, this math anxiety relief was more prominent in those with high‐level math anxiety. Although the children who underwent the training showed training‐induced math achievement and working memory enhancement, this cognitive improvement appeared to be independent of the math anxiety relief.Conclusion: Our findings demonstrate that students can show highly negative emotions and perceptions toward learning even in high‐achieving countries. Auspiciously, the feeling of distress toward learning has the feasibility to be relieved from short‐term intensive training. Our study suggests a new approach of early treatments to emotional disturbance that can lead to permanent consequences in individuals.

Acid stress is one of the most common adverse conditions during fermentation of fruit wines, and the acid tolerance of yeasts is, therefore, critical for f

Acid tolerance and aroma profile are crucial factors for wine production in Saccharomyces cerevisiae. However, most wine yeasts to date fail to endure low-pH environments, therefore resulting in problems such as lengthened fermentation and poor flavor during acidic fruit wine production. In the present study, we established a multiple-step screening strategy, which was composed of atmospheric and room temperature plasma (ARTP), high-throughput screening (HTS), and laboratory adaptive evolution (ALE), to screen yeast strains for potential wine-producing with enhanced performances during low pH conditions. Importantly, we obtained the S. cerevisiae strain from the mutant library, ET008-c54, which displayed exhibited excellent performances in survival rate, fermentation time, aroma profile, and genetic stability. More specifically, the survival rate of ET008-c54 at low pH was increased by 10-fold, the fermentation time of greengage plum wine was shortened by about 70%, and the content of main aroma compounds was significantly increased by 52%. Collectively, we demonstrate the practical application of the screening platform designed for discovering mutant strains in winemaking technology. Key Points • A novel multi-step method was developed for screening wine-producing yeast. • Acid tolerance and aroma profiles of yeast were improved by the proposed method. • ET008-c54 showed excellent fermenting performance and stability at low pH.

Mathematical word problems are ubiquitous and standard for teaching and evaluating generalization of mathematical knowledge for real‐world contexts. It is therefore concerning that the neural mechanisms of word problem solving are not well understood, as these insights represent strong potential for improving education and remediating deficits in this domain. Here, we investigate neural response to word problems via functional magnetic resonance imaging (fMRI). Healthy adults performed sentence judgment tasks on word problems that either contained one‐step mathematical operations, or nonarithmetic judgments on parallel narratives without any numerical information. Behavioral results suggested that the composite efficiency measurement of combining accuracy and RT did not differ between the two problem types. Arithmetic sentence judgments elicited greater activation in the fronto‐insular‐parietal network including intraparietal sulcus (IPS), dorsolateral prefrontal cortex (PFC), and anterior insula (AI) than narrative sentence judgment. Narrative sentence judgments, conversely, resulted in greater activation predominantly in the left ventral PFC, angular gyrus and perisylvian cortex compared with reading arithmetic sentences. Moreover, task‐dependent functional connectivity analyses showed the AI circuits were more strongly coupled with IPS during arithmetic sentence judgments than nonarithmetic sentences. Finally, activations in the IPS during arithmetic were highly correlated with out‐of‐scanner performance on a distinct set of problems with the same characteristics. These results show arithmetic word problem performance differences may rely more heavily on fronto‐insular‐parietal circuits for mathematical model building than narrative text comprehension of similar difficulty. More broadly, our study suggests that quantitative measurements of brain mechanisms can provide pivotal role for uncovering crucial arithmetic skills.