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Blowing agent-assisted extrusion cooking is a novel processing technique that can alter the expansion of extruded snacks and, thus, enhance their physical appeal, such as texture. However, to this day, this technique has only been studied for ingredients with limited protein contents (<30%). In this study, protein-enriched snacks were extruded using nitrogen gas as a blowing agent at a wide protein range (0–50%) to better explore the potential of this technique in manufacturing high-protein snacks. The results showed that, with nitrogen gas injection, extrudate radial expansion was significantly (p < 0.05) improved at 10% and 40% protein, while extrudate density was significantly reduced at 30% and 50% protein. Nitrogen gas-injected extrudates, especially at 50% protein, exhibited improvements in texture, including a reduction in hardness and an increase in crispness. Collectively, this study showed the promising potential of nitrogen gas-assisted extrusion in improving the physical appeal of innovative healthy snacks at a high protein level (i.e., 50%). [ABSTRACT FROM AUTHOR]

Visual Question Answering (VQA) models fail catastrophically on questions related to the reading of text-carrying images. However, TextVQA aims to answer questions by understanding the scene texts in an image–question context, such as the brand name of a product or the time on a clock from an image. Most TextVQA approaches focus on objects and scene text detection, which are then integrated with the words in a question by a simple transformer encoder. The focus of these approaches is to use shared weights during the training of a multi-modal dataset, but it fails to capture the semantic relations between an image and a question. In this paper, we proposed a Scene Graph-Based Co-Attention Network (SceneGATE) for TextVQA, which reveals the semantic relations among the objects, the Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image. We create a guided-attention module to capture the intra-modal interplay between the language and the vision as a guidance for inter-modal interactions. To permit explicit teaching of the relations between the two modalities, we propose and integrate two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention. We conduct extensive experiments on two widely used benchmark datasets, Text-VQA and ST-VQA. It is shown that our SceneGATE method outperforms existing ones because of the scene graph and its attention modules. [ABSTRACT FROM AUTHOR]

The potential utilization of yellow pea flour and bread crumb blends was investigated to generate nutritionally‐dense extruded products with superior physical and/or technofunctional properties. Yellow pea flour mixed with bread crumb at different ratios were processed using low‐moisture twin‐screw extrusion cooking conditions to examine the effect of blending ratios and feed moisture contents on physical (that is, radial expansion index, extrudate density, microstructure, texture, and color) and technofunctional (that is, emulsifying capacity, emulsifying stability, water solubility [WS], water binding capacity [WBC], oil binding capacity [OBC], and pasting) properties of the final products. Compared to the two feed materials alone, samples produced with yellow pea flour and bread crumb blends showed lower hardness and higher crispiness. Moisture content (12% to 18%) was found to significantly affect physical and technofunctional properties. With an increase in feed moisture content from 12% to 18%, the WBC of the extrudates increased while the WS decreased. Extrudates produced with higher feed moisture content, and higher yellow pea flour content had higher setback viscosity. Among all formulas and feed moisture contents studied, extrudates produced with 50% yellow pea flour and 50% bread crumb at 12% feed moisture content had the highest radial expansion and bigger cells with thinner cell walls. This study has shown that incorporation of yellow pea flour and bread crumb in extrusion cooking process could be used to develop nutritionally‐dense foods with improved physical and technofunctional properties. [ABSTRACT FROM AUTHOR]

Traditionally, the diagnosis of Parkinson's disease (PD) has been made based on symptoms. Extensive studies have demonstrated that PD may lead to variation of brain activity in different frequency bands. However, frequency specific dynamic alterations of PD have not yet been explored. In order to address this gap, a novel sparse nonnegative tensor decomposition (SNTD) method was used to estimate frequency specific co-activation patterns (CAP). The difference between PD and healthy controls (HC) are investigated with the proposed framework. The difference between PD and HC mainly exists at frequency band 0.04–0.1 Hz in basal ganglia. We also found that the average intensity of PD in this frequency band is significantly correlated with the Hoehn and Yahr scale. Compared with conventional CAP approach, SNTD estimates frequency specific CAPs that show alterations in PD patients. SNTD provides an alternative to K-means clustering used in conventional CAP analysis. With the proposed framework, frequency specific CAPs are extracted, and alterations in PD patients are also successfully discovered. • A novel frequency specific co-activation pattern analysis framework was proposed. • Alternations of Parkinson's disease were detected with the proposed framework. • We proved that sparse nonnegative tensor decomposition provides an alternative to K-means clustering. [ABSTRACT FROM AUTHOR]