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Rice bran dietary fiber (RBDF) is a byproduct of rice milling that has potential applications in breadmaking. This study investigated the effects of adding RBDF at different soluble and insoluble fractions on bread quality and storage properties. RBDF was separated into soluble (RBSDF) and insoluble (RBIDF) fractions and added to the bread at various concentrations. The results showed that adding an appropriate amount of RBSDF (6%) improved the texture, specific volume, sensory evaluation, and shelf life of bread while adding too much RBSDF (12%) or RBIDF (>8%) reduced the quality of bread and increased its hardening and moisture loss during storage. The bread quality was improved when the addition of RBSDF and RBIDF were 6% and 5%, respectively, which could enhance the fiber intake, meeting the diversified dietary needs of consumers. [Display omitted] • Rice bran dietary fiber (RBDF) has potential applications in bread making. • Adding 6% RBSDF improves bread's texture, sensory evaluation, and shelf life. • Too much RBSDF (12%) or RBIDF (>8%) increased hardening and moisture loss. • Optimal adding contents of RBSDF and RBIDF are 6% and 5%, respectively. [ABSTRACT FROM AUTHOR]

The P300 event-related potential (ERP) varies across individuals, and exploring this variability deepens our knowledge of the event, and scope for its potential applications. Previous studies exploring the P300 have relied on either electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). We applied simultaneous event-related EEG-fMRI to investigate how the network structure is updated from rest to the P300 task so as to guarantee information processing in the oddball task. We first identified 14 widely distributed regions of interest (ROIs) that were task-associated, including the inferior frontal gyrus and the middle frontal gyrus, etc. The task-activated network was found to closely relate to the concurrent P300 amplitude, and moreover, the individuals with optimized resting-state brain architectures experienced the pruning of network architecture, i.e. decreasing connectivity, when the brain switched from rest to P300 task. Our present simultaneous EEG-fMRI study explored the brain reconfigurations governing the variability in P300 across individuals, which provided the possibility to uncover new biomarkers to predict the potential for personalized control of brain-computer interfaces. • Simultaneous EEG-fMRI is used to study P300 variability during the oddball task. • Network reconfiguration helps to uncover the P300 variability. • Low P300 group shows enhanced connectivity deviating from resting state. • The smaller the network updates, the larger the P300 amplitude. [ABSTRACT FROM AUTHOR]

Planned urban form has become an important strategy to improve air quality in urban agglomerations (UAs), especially pollution due to PM 2.5 , but the influencing mechanisms are not yet clear. This study explores the relationship between four metrics of urban form (size, fragmentation, shape, and dispersion) as determined by analysis of remotely sensed images at 30-m resolution and PM 2.5 concentrations in 19 Chinese UAs. The influence of level of urban development and season is examined. Five control variables, including population density, temperature, precipitation, wind speed, and the normalized difference vegetation index (NDVI) are selected for use in multiple linear regression models. Size, fragmentation, and shape of urban form, but not dispersion, were found to have significant effects on PM 2.5 concentrations of different urbanization-level UAs. Urban size and fragmentation have stronger impacts on PM 2.5 concentrations in UAs with lower urbanization levels while urban shape has a greater impact in higher-level UAs. In terms of seasonal variation in all UAs, urban form is more pronouncedly associated with PM 2.5 concentrations during spring and autumn than summer and winter. Urban size and fragmentation are positively associated with PM 2.5 concentrations whereas urban shape and dispersion are on the contrary. The relationships between urban form and PM 2.5 uncovered here underscore the importance of urban planning as a tool to minimize PM 2.5 pollution. Specifically, local government should encourage polycentric urban form with lower fragmentation in urban agglomerations. UAs with lower urbanization levels should control the disordered expansion of construction land and higher-level UAs should promote the mix of green land and construction land. Moreover, measures to control air pollution from anthropogenic activities in spring, autumn and winter are likely to be more effective in decreasing PM 2.5 concentrations in UAs. • The urban form was measured by urban size, fragmentation, shape, and dispersion. • Effect of urban form on PM 2.5 concentrations varies with urbanization levels of urban agglomerations. • Seasonally, urban form has a stronger effect on PM 2.5 concentrations in spring and autumn. • Polycentric urban form with lower fragmentation should be encouraged in urban agglomerations for reducing PM 2.5 pollution. [ABSTRACT FROM AUTHOR]

Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual's decision-making response (i.e., acceptance or rejection). We proposed an electroencephalogram (EEG)-based computational intelligence framework to predict individual responses. Specifically, the discriminative spatial network pattern (DSNP), a supervised learning approach, was applied to single-trial EEG data to extract the DSNP feature from the single-trial brain network. A linear discriminate analysis (LDA) trained on the DSNP features was then used to predict the individual response trial-by-trial. To verify the performance of the proposed DSNP, we recruited two independent subject groups, and recorded the EEGs using two types of EEG systems. The performances of the trial-by-trial predictors achieved an accuracy of 0.88 ± 0.09 for the first dataset, and 0.90 ± 0.10 for the second dataset. These trial-by-trial prediction performances suggested that individual responses could be predicted trial-by-trial by using the specific pattern of single-trial EEG networks, and our proposed method has the potential to establish the biologically inspired artificial intelligence decision system. • A single-trial EEG based approach is developed to predict individual decision-making response. • A supervised learning approach is developed to extract the discriminative spatial network pattern. • The proposed approach achieves the high prediction performances (over 0.88) for two independent EEG datasets. [ABSTRACT FROM AUTHOR]