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While the close relationship between the brain system for speech processing and reading development is well-documented in alphabetic languages, whether and how such a link exists in children in a language without systematic grapheme-phoneme correspondence has not been directly investigated. In the present study, we measured Chinese children's brain activation during an auditory lexical decision task with functional magnetic resonance imaging. The results showed that brain areas distributed across the temporal and frontal lobes activated during spoken word recognition. In addition, the left occipitotemporal cortex (OTC) was recruited, especially under the real word condition, thus confirming the involvement of this orthographic-related area in spoken language processing in Chinese children. Importantly, activation of the left temporoparietal cortex (TPC) in response to words and pseudowords was positively correlated with children's reading ability, thus supporting the salient role phonological processing plays in Chinese reading in the developing brain. Furthermore, children with higher reading scores also increasingly recruited the left anterior OTC to make decisions on the lexical status of pseudowords, indicating that higher-skill children tend to search abstract lexical representations more deeply than lower-skill children in deciding whether spoken syllables are real. In contrast, the precuneus was more related to trial-by-trial reaction time in lower-skill children, suggesting that effort-related neural systems differ among pupils with varying reading abilities. Taken together, these findings suggest a strong link between the neural correlates of speech processing and reading ability in Chinese children, thus supporting a universal basis underlying reading development across languages.

Majorbio Cloud is an online analytic platform that aims to promote the development of bioinformatics services and accelerate discoveries in the life sciences community. In 2024, the platform released new workflows and extensions to facilitate the mining and interpretation of omics data. These workflows include single-cell transcriptomics, proteomics, and metabolomics. The platform provides user-friendly pipelines for data processing, analysis, and interpretation in each of these areas. Majorbio Cloud has been widely used by researchers around the world and offers an interactive communication and knowledge dispersion platform. [Extracted from the article]

Background: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. Methods: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5–10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. Results: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1–4 and 5–7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. Conclusion: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis. [ABSTRACT FROM AUTHOR]

Compressive performances of composite structures can be significantly decreased due to the lightning strike damage, so the structures with lightning frighten must be considered lightning protection design. In this study, compressive experiments after lightning strikes were conducted on non‐protected, 12‐layer interlaminar carbon nanotube film (CNF) protected, and traditional surface silver coating (TSSC)‐protected composite laminates. A numerical analysis procedure was established, incorporating a lightning strike ablation damage simulation (LSADS) module and compressive residual strength calculation (CRSC) module. The procedure's effectiveness was verified by the experiment results. Based on this procedure, the compressive performances and the possible failure mechanism of laminates after lightning strikes were analyzed. The results show that the laminates with TSSC protection and 12‐layer interlaminar CNF protection can increase compressive residual strength compared with non‐protected laminates. The predominant compressive damages of the laminates after lightning strikes are fiber‐matrix shear damage and fiber fracture damage. Optimized one‐layer interlaminar CNF protection with a thickness of 0.36 mm can increase the laminate compressive strength but decrease the structural weight. This study offers a reference and basis for the lightning strike protection design of composite structures. Highlights: CNF in lightning strike protection of composite structures.Compressive failure mechanism of composite laminates after lightning strikes.A numerical procedure to evaluate lightning damage and compressive performances.Design of interlaminar CNF protection. [ABSTRACT FROM AUTHOR]

Based on the traditional black tea processing technology, the hot air withering process was used to replace the traditional indoor natural withering process on the basis of the traditional black tea processing technology. The effects of four hot air withering processes of 35 °, 45 °, 55 ° and 65 ° on the quality of summer and autumn black tea were studied and analyzed from three aspects of sensory quality, taste quality and aroma quality. The results show that compared with 25 ° traditional indoor natural withering black tea, the comprehensive sensory quality of black tea processed by 45 ° hot air withering process was better. The color of dry tea was dark brown and moist, the soup color was red and bright, the taste was mellow and refreshing, and the quality characteristics was sweet aroma. At the same time, the contents of tea polyphenols, gallic acid, EGCG, theaflavins, thearubigins and the ratio of the sum contents of theaflavins and thearubigins to the content of theabrownins (TFRB) significantly increased (P<0.05), while the content of soluble sugar decreased significantly (P<0.05). The types of aroma quality components increased, among which alcohols accounted for the highest proportion (70.47%), and the relative contents of alcohols, ketones and esters increased significantly (P<0.05), while pyrroles and aldehydes decreased significantly (P < 0.05). And the aroma components with flower and fruit aroma such as trans-3-hexenol, 2,3-dihydro-2,2,6-trimethylbenzaldehyde and α-ionone were detected. This shows that hot air withering with appropriate temperature is conducive to improving the quality of black tea, which can be applied to the production practice of improving the quality of black tea in summer and autumn. [ABSTRACT FROM AUTHOR]

Soil bulk density (BD) serves as a fundamental indicator of soil health and quality, exerting a significant influence on critical factors such as plant growth, nutrient availability, and water retention. Due to its limited availability in soil databases, the application of pedotransfer functions (PTFs) has emerged as a potent tool for predicting BD using other easily measurable soil properties, while the impact of these PTFs' performance on soil organic carbon (SOC) stock calculation has been rarely explored. In this study, we proposed an innovative local modeling approach for predicting BD of fine earth (BDfine) across Europe using the recently released BDfine data from the LUCAS Soil (Land Use and Coverage Area Frame Survey Soil) 2018 (0–20 cm) and relevant predictors. Our approach involved a combination of neighbor sample search, forward recursive feature selection (FRFS), and random forest (RF) models (local- RFFRFS). The results showed that local- RFFRFS had a good performance in predicting BDfine (R2 of 0.58, root mean square error (RMSE) of 0.19 gcm-3 , relative error (RE) of 16.27 %), surpassing the earlier-published PTFs (R2 of 0.40–0.45, RMSE of 0.22 gcm-3 , RE of 19.11 %–21.18 %) and global PTFs using RF models with and without FRFS (R2 of 0.56–0.57, RMSE of 0.19 gcm-3 , RE of 16.47 %–16.74 %). Interestingly, we found that the best earlier-published PTF (R2 = 0.84, RMSE = 1.39 kgm-2 , RE of 17.57 %) performed close to the local- RFFRFS (R2 = 0.85, RMSE = 1.32 kgm-2 , RE of 15.01 %) in SOC stock calculation using BDfine predictions. However, the local- RFFRFS still performed better (ΔR2 > 0.2) for soil samples with low SOC stocks (< 3 kgm-2). Therefore, we suggest that the local- RFFRFS is a promising method for BDfine prediction, while earlier-published PTFs would be more efficient when BDfine is subsequently utilized for calculating SOC stock. Finally, we produced two topsoil BDfine and SOC stock datasets (18 945 and 15 389 soil samples) at 0–20 cm for LUCAS Soil 2018 using the best earlier-published PTF and local- RFFRFS , respectively. This dataset is archived on the Zenodo platform at https://doi.org/10.5281/zenodo.10211884 (S. Chen et al., 2023). The outcomes of this study present a meaningful advancement in enhancing the predictive accuracy of BDfine , and the resultant BDfine and SOC stock datasets for topsoil across the Europe enable more precise soil hydrological and biological modeling. [ABSTRACT FROM AUTHOR]