The cause of attention modulations in bilingual vocabulary contexts

The limiting AA in both raw and roasted pistachio nuts that determined the DIAAS for this age group was Lys. CONCLUSION Results of this research illustrate that raw and roasted pistachio nuts can be considered a good quality protein source with DIAAS greater than 75, however, processing conditions associated with roasting may decrease the digestibility of AA in pistachio nuts. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.BACKGROUND AND PURPOSE The purpose of this study is to develop a deep learning-based approach to simultaneously segment five pelvic organs including prostate, bladder, rectum, left and right femoral heads on cone-beam CT (CBCT), as required elements for prostate adaptive radiotherapy planning. MATERIALS AND METHODS We propose to utilize both CBCT and CBCT-based synthetic MRI (sMRI) for the segmentation of soft tissue and bony structures, as they provide complementary information for pelvic organ segmentation. CBCT images have superior bony structure contrast and sMRIs have superior soft tissue contrast. Prior to segmentation, sMRI was generated using a cycle-consistent adversarial networks (CycleGAN), which was trained using paired CBCT-MR images. To combine the advantages of both CBCT and sMRI, we developed a cross-modality attention pyramid network with late feature fusion. Our method processes CBCT and sMRI inputs separately to extract CBCT-specific and sMRI-specific features prior to combining them in a ld be used in the clinic to support rapid target and organs-at-risk contouring for prostate adaptive radiation therapy. © 2020 American Association of Physicists in Medicine.BACKGROUND Dysphagia is defined as a disorder of the swallowing mechanism. The most common management of dysphagia is diet modification by thickening food and beverages. This study aimed to obtain protein-based beverages for the dysphagia diets of the elderly, corresponding to the honey (III) level of dysphagia fluids according to the National Dysphagia Diet classifications and containing 100 g kg-1 of good-quality proteins with a high rate of hydrolysis during digestion. RESULTS Four protein formulations made from pea proteins, milk proteins, mixture of milk and pea proteins, as well as milk proteins with added konjac glucomannan have been evaluated on the basis of rheological characterisation and proteolysis kinetics during in vitro digestion. The mixture of milk proteins and pea proteins and milk proteins with added konjac glucomannan showed typical yielding pseudoplastic fluid behaviour with similar apparent viscosity but different structural characteristics. This condition was the reason for the differences in proteolysis kinetics during digestion. The mixture of milk and pea proteins showed viscous liquid behaviour and was more rapidly hydrolysed under gastrointestinal conditions than those containing milk proteins and konjac glucomannan acting as a weak gel system. CONCLUSION We presume that geriatric consumers with swallowing difficulties may benefit from honey-viscosity protein-based beverages containing pea and milk proteins through faster proteolysis and better bioaccessibility of amino acids during digestion. This article is protected by copyright. All rights reserved. This article is protected by copyright. selleck chemical All rights reserved.Plant pathogens use effector proteins to promote host colonization. The mode of action of effectors from root invading pathogens, such as Fusarium oxysporum (Fo), is poorly understood. Here, we investigated whether Fo effectors suppress pattern-triggered immunity (PTI), and whether they enter host cells during infection. - Eight candidate effectors of an Arabidopsis-infecting Fo strain were expressed with and without signal peptide in Nicotiana benthamiana and their effect on flg22- and chitin-triggered ROS burst was monitored. To detect uptake, effector biotinylation by an intracellular Arabidopsis-produced biotin ligase was examined following root infection. - Four effectors suppressed PTI signaling; two act intracellularly and two apoplastically. Heterologous expression of a PTI-suppressing effector in Arabidopsis enhanced bacterial susceptibility. Consistent with an intracellular activity, host cell uptake of five effectors, but not of the apoplastically acting ones, was detected in Fo infected Arabidopsis roots. - Multiple Fo effectors target PTI signaling, uncovering a surprising overlap in infection strategies between foliar- and root pathogens. Extracellular targeting of flg22 signaling by a microbial effector provides a new mechanism on how plant pathogens manipulate their host. Effector translocation appears independent of protein size, charge, presence of conserved motifs or the promoter driving its expression. This article is protected by copyright. All rights reserved.In August 2019, visual inspection of intertidal zones of the Gulf of Maine (ME, USA) revealed young and adult wild blue mussels, Mytilus spp., in Alley Bay (Jonesport area) with the distinctive L-shaped shell deformity (LSSD) and green spots (GS) in the mantle and adductor muscle. LSSD is a characteristic sign of current or previous mussel infection by photosynthetic unicellular alga from the group Coccomyxa, while GS are algal colonies. Based on these findings, this study represents the first report of infection signs by pathogenic Coccomyxa-like algae in mussels from the coastal waters of the Northeastern United States, providing a base for future large scale monitoring of the alga in the region. © 2020 John Wiley & Sons Ltd.PURPOSE An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy because Bluetooth signal is subject to significant fluctuation. We aim to improve the accuracy of RTLS using the deep learning technique. METHODS We installed a Bluetooth sensor network in a three-floor clinic building to track patients, staff, and devices. The Bluetooth sensors measured the strength of the signal broadcasted from Bluetooth tags, which was fed into a deep neural network to calculate the location of the tags. The proposed deep neural network consists of a long short-term memory (LSTM) network and a deep classifier for tracking moving objects. Additionally, a spatial-temporal constraint algorithm was implemented to further increase the accuracy and stability of the results. To train the neural network, we divided the building into 115 zones and collected training data in each zone.