211 research outputs found
Physico-chemical properties and stability of lipid droplet-stabilised emulsions : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Manawatū, New Zealand
It is known that the structure of the interfacial layer impacts the stability and the function of emulsions. Hierarchical emulsions, known as droplet-stabilized emulsions (DSEs), were made from nano-sized primary oil droplets that coated with protein particles for potentially advanced functionality. In this study, the primary droplets were made of either rigid (whey protein microgel, WPM) or soft protein (Ca²⁺-cross-linked caseinate, Ca-CAS) particles. The structure of the protein particles and primary droplets in solution and at the oil-water interface were characterised; the oil exchange process between the surface and core oil droplets were examined, using light scattering, microscopy, small angle scattering, ultra-small angle scattering techniques, etc. The emulsification capacity of the primary emulsion has been shown to be improved by using soft and flexible protein particles, resulting in small droplet sizes and smooth interfacial layers of the DSE. The droplet-stabilised interfacial layer has been shown to provide DSE a good stability against coalescence during gastric enzymatic hydrolysis, long-term storage, and heating, as well as improved functionalities in the rate of the lipolysis during simulated intestinal digestion and the rheological properties at high oil content.
Overall, this research provided new information on DSE physical-chemical properties and stability as affected by the structure of emulsifiers (protein particles and the subsequent primary droplets), digestion destabilisation, pH, storage time and temperature. The outcomes have potential for designing functional foods with improved active compound delivery and mechanical strength
Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder
IntroductionThe exorbitant cost of accurately annotating the large-scale serial scanning electron microscope (SEM) images as the ground truth for training has always been a great challenge for brain map reconstruction by deep learning methods in neural connectome studies. The representation ability of the model is strongly correlated with the number of such high-quality labels. Recently, the masked autoencoder (MAE) has been shown to effectively pre-train Vision Transformers (ViT) to improve their representational capabilities.MethodsIn this paper, we investigated a self-pre-training paradigm for serial SEM images with MAE to implement downstream segmentation tasks. We randomly masked voxels in three-dimensional brain image patches and trained an autoencoder to reconstruct the neuronal structures.Results and discussionWe tested different pre-training and fine-tuning configurations on three different serial SEM datasets of mouse brains, including two public ones, SNEMI3D and MitoEM-R, and one acquired in our lab. A series of masking ratios were examined and the optimal ratio for pre-training efficiency was spotted for 3D segmentation. The MAE pre-training strategy significantly outperformed the supervised learning from scratch. Our work shows that the general framework of can be a unified approach for effective learning of the representation of heterogeneous neural structural features in serial SEM images to greatly facilitate brain connectome reconstruction
Regional association of pCASL-MRI with FDG-PET and PiB-PET in people at risk for autosomal dominant Alzheimer's disease.
Autosomal dominant Alzheimer's disease (ADAD) is a small subset of Alzheimer's disease that is genetically determined with 100% penetrance. It provides a valuable window into studying the course of pathologic processes that leads to dementia. Arterial spin labeling (ASL) MRI is a potential AD imaging marker that non-invasively measures cerebral perfusion. In this study, we investigated the relationship of cerebral blood flow measured by pseudo-continuous ASL (pCASL) MRI with measures of cerebral metabolism (FDG PET) and amyloid deposition (Pittsburgh Compound B (PiB) PET). Thirty-one participants at risk for ADAD (age 39 ± 13 years, 19 females) were recruited into this study, and 21 of them received both MRI and FDG and PiB PET scans. Considerable variability was observed in regional correlations between ASL-CBF and FDG across subjects. Both regional hypo-perfusion and hypo-metabolism were associated with amyloid deposition. Cross-sectional analyses of each biomarker as a function of the estimated years to expected dementia diagnosis indicated an inverse relationship of both perfusion and glucose metabolism with amyloid deposition during AD development. These findings indicate that neurovascular dysfunction is associated with amyloid pathology, and also indicate that ASL CBF may serve as a sensitive early biomarker for AD. The direct comparison among the three biomarkers provides complementary information for understanding the pathophysiological process of AD
Decoupled Mixup for Data-efficient Learning
Mixup is an efficient data augmentation approach that improves the
generalization of neural networks by smoothing the decision boundary with mixed
data. Recently, dynamic mixup methods have improved previous static policies
effectively (e.g., linear interpolation) by maximizing salient regions or
maintaining the target in mixed samples. The discrepancy is that the generated
mixed samples from dynamic policies are more instance discriminative than the
static ones, e.g., the foreground objects are decoupled from the background.
However, optimizing mixup policies with dynamic methods in input space is an
expensive computation compared to static ones. Hence, we are trying to transfer
the decoupling mechanism of dynamic methods from the data level to the
objective function level and propose the general decoupled mixup (DM) loss. The
primary effect is that DM can adaptively focus on discriminative features
without losing the original smoothness of the mixup while avoiding heavy
computational overhead. As a result, DM enables static mixup methods to achieve
comparable or even exceed the performance of dynamic methods. This also leads
to an interesting objective design problem for mixup training that we need to
focus on both smoothing the decision boundaries and identifying discriminative
features. Extensive experiments on supervised and semi-supervised learning
benchmarks across seven classification datasets validate the effectiveness of
DM by equipping it with various mixup methods.Comment: The preprint revision, 15 pages, 6 figures. The source code is
available at https://github.com/Westlake-AI/openmixu
Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View
Spatio-temporal predictive learning plays a crucial role in self-supervised
learning, with wide-ranging applications across a diverse range of fields.
Previous approaches for temporal modeling fall into two categories:
recurrent-based and recurrent-free methods. The former, while meticulously
processing frames one by one, neglect short-term spatio-temporal information
redundancies, leading to inefficiencies. The latter naively stack frames
sequentially, overlooking the inherent temporal dependencies. In this paper, we
re-examine the two dominant temporal modeling approaches within the realm of
spatio-temporal predictive learning, offering a unified perspective. Building
upon this analysis, we introduce USTEP (Unified Spatio-TEmporal Predictive
learning), an innovative framework that reconciles the recurrent-based and
recurrent-free methods by integrating both micro-temporal and macro-temporal
scales. Extensive experiments on a wide range of spatio-temporal predictive
learning demonstrate that USTEP achieves significant improvements over existing
temporal modeling approaches, thereby establishing it as a robust solution for
a wide range of spatio-temporal applications.Comment: Under revie
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Accurate prediction of protein-ligand binding structures, a task known as
molecular docking is crucial for drug design but remains challenging. While
deep learning has shown promise, existing methods often depend on holo-protein
structures (docked, and not accessible in realistic tasks) or neglect pocket
sidechain conformations, leading to limited practical utility and unrealistic
conformation predictions. To fill these gaps, we introduce an under-explored
task, named flexible docking to predict poses of ligand and pocket sidechains
simultaneously and introduce Re-Dock, a novel diffusion bridge generative model
extended to geometric manifolds. Specifically, we propose energy-to-geometry
mapping inspired by the Newton-Euler equation to co-model the binding energy
and conformations for reflecting the energy-constrained docking generative
process. Comprehensive experiments on designed benchmark datasets including
apo-dock and cross-dock demonstrate our model's superior effectiveness and
efficiency over current methods
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