DEVELOPMENT OF COLLOIDS-BASED FOOD DELIVERY SYSTEMS TO ENHANCE THE BENEFICIAL EFFECTS OF NUTRACEUTICALS FOR HUMAN HEALTH

In current studies, hydrogel particles were formed by polysaccharides (i.e., alginate or pectin) and protein (i.e., casein or gelatin) through complex coacervation. Our results indicated that the encapsulation of protein or polyunsaturated lipid droplets nanoparticles within the hydrogel particles could improve their chemical or physical stability during storage. The lipid droplets encapsulated within the hydrogel particles could be released under simulated oral conditions, which was triggered by a pH or temperature change. The current study further fabricated the hydrogel particles (beads) using alginate or carrageenan based on an injection-gelation method. We found carrageenan beads had a relatively fragile structure that was easily disrupted in the GIT and released the encapsulated lipid droplets and curcumin. Conversely, alginate beads had a robust structure that remained relatively intact throughout the GIT and retained the lipid droplets and curcumin. The incorporation of the β-carotene-loaded lipid droplets into hydrogel beads greatly improved its chemical stability during storage and digestion process. The current study further fabricated the hydrogel beads with self-regulating internal pH microclimates by encapsulating antacid agents (i.e., Mg(OH)2or CaCO3) inside them. A quantitative fluorescence confocal laser scanning microscopy (CLSM) method was developed to map the local pH inside the hydrogel beads. Our results showed that the pH inside antacid-loaded beads remained close to neutral in the mouth and stomach, leading to retention the activity of acid sensitive agents (i.e., lactase, lipase, insulin and probiotics) in the small intestine. The current study further studied the effect of pH on the encapsulation, retention and release of whey proteins from alginate-based hydrogel beads. Our results indicated that the protein encapsulation efficiency and retention of the beads increased with decreasing fabrication pH, which was attributed to the fact that there was a strong electrostatic attraction between the cationic protein and anionic beads matrix (alginate molecules) at low pH conditions. Overall, our study suggest that hydrogel particles with different structures and compositions can be designed for encapsulation, protection, and delivery of hydrophilic or lipophilic bioactive agents, which is advantageous for the development of certain functional food products

Hyper-Relational Knowledge Graph Neural Network for Next POI

With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing works employ Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These approaches primarily focus on modeling the pair-wise relations in LBSN to enrich the semantics and thereby relieve the data sparsity issue. However, existing approaches seldom consider the hyper-relations in LBSN, such as the mobility relation (a 3-ary relation: user-POI-time). This makes the model hard to exploit the semantics accurately. In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity.To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed to maintain and exploit the rich semantics of hyper-relations. Then we proposed a Hypergraph Neural Network to utilize the structural information of HKG in a cohesive way. In addition, a self-attention network is used to leverage sequential information and make personalized recommendations. Furthermore, side information, essential in reducing data sparsity by providing background knowledge of POIs, is not fully utilized in current methods. In light of this, we extended the current dataset with available side information to further lessen the impact of data sparsity. Results of experiments on four real-world LBSN datasets demonstrate the effectiveness of our approach compared to existing state-of-the-art methods

Impact of Pesticide Type and Emulsion Fat Content on the Bioaccessibility of Pesticides in Natural Products

There is interest in incorporating nanoemulsions into certain foods and beverages, including dips, dressings, drinks, spreads, and sauces, due to their potentially beneficial attributes. In particular, excipient nanoemulsions can enhance the bioavailability of nutraceuticals in fruit- and vegetable-containing products consumed with them. There is, however, potential for them to also raise the bioavailability of undesirable substances found in these products, such as pesticides. In this research, we studied the impact of excipient nanoemulsions on the bioaccessibility of pesticide-treated tomatoes. We hypothesized that the propensity for nanoemulsions to raise pesticide bioaccessibility would depend on the polarity of the pesticide molecules. Bendiocarb, parathion, and chlorpyrifos were therefore selected because they have Log P values of 1.7, 3.8, and 5.3, respectively. Nanoemulsions with different oil contents (0%, 4%, and 8%) were fabricated to study their impact on pesticide uptake. In the absence of oil, the bioaccessibility increased with increasing pesticide polarity (decreasing Log P): bendiocarb (92.9%) \u3e parathion (16.4%) \u3e chlorpyrifos (2.8%). Bendiocarb bioaccessibility did not depend on the oil content of the nanoemulsions, which was attributed to its relatively high water-solubility. Conversely, the bioaccessibility of the more hydrophobic pesticides (parathion and chlorpyrifos) increased with increasing oil content. For instance, for chlorpyrifos, the bioaccessibility was 2.8%, 47.0%, and 70.7% at 0%, 4%, and 8% oil content, respectively. Our findings have repercussions for the utilization of nanoemulsions as excipient foods in products that may have high levels of undesirable non-polar substances, such as pesticides

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, from easy to complex, can provide performance improvements over traditional training procedure. The idea has its root in Curriculum Learning which suggests in the early stage of training models can be sensitive to noise and difficult samples. In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. Specifically, we evaluate the learning difficulty of each node in high-level feature space and drop those difficult ones out to ensure the model only needs to handle fundamental ST relations at the beginning, before gradually moving to hard ones. Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization

Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere

Due to the lack of more efficient diagnostic tools for monkeypox, its spread remains unchecked, presenting a formidable challenge to global health. While the high efficacy of deep learning models for monkeypox diagnosis has been demonstrated in related studies, the overlook of inference speed, the parameter size and diagnosis performance for early-stage monkeypox renders the models inapplicable in real-world settings. To address these challenges, we proposed an ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possesses only 0.27M parameters and can process input images at 68 frames per second (FPS) on the CPU. To counteract the diagnostic performance limitation brought about by the small model capacity, it integrates the attention-based feature fusion module and the multiple auxiliary losses enhancement strategy for better detecting subtle image changes and optimizing weights. Using transfer learning and five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on the Mpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. By adopting data augmentation, our model's Accuracy rises to 98.40% and attains a Practicality Score (A new metric for measuring model practicality in real-time diagnosis application) of 0.80. We also developed an application system named Mpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2 features ultrafast responses, offline functionality, and easy deployment, enabling accurate and real-time diagnosis for both the public and individuals in various real-world settings, especially in populous settings during the outbreak. Our work could potentially mitigate future monkeypox outbreak and illuminate a fresh paradigm for developing real-time diagnostic tools in the healthcare field

A universal optical modulator for synthetic topologically tuneable structured matter

Topologically structured matter, such as metasurfaces and metamaterials, have given rise to impressive photonic functionality, fuelling diverse applications from microscopy and holography to encryption and communication. Presently these solutions are limited by their largely static nature and preset functionality, hindering applications that demand dynamic photonic systems with reconfigurable topologies. Here we demonstrate a universal optical modulator that implements topologically tuneable structured matter as virtual pixels derived from cascading low functionality tuneable devices, altering the paradigm of phase and amplitude control to encompass arbitrary spatially varying retarders in a synthetic structured matter device. Our approach opens unprecedented functionality that is user-defined with high flexibility, allowing our synthetic structured matter to act as an information carrier, beam generator, analyser, and corrector, opening an exciting path to tuneable topologies of light and matter

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival