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

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsupervised manner. Despite remarkable progress in recent years, a lack of systematic understanding persists due to the diverse settings, complex implementation, and difficult reproducibility. Without standardization, comparisons can be unfair and insights inconclusive. To address this dilemma, we propose OpenSTL, a comprehensive benchmark for spatio-temporal predictive learning that categorizes prevalent approaches into recurrent-based and recurrent-free models. OpenSTL provides a modular and extensible framework implementing various state-of-the-art methods. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and weather forecasting. Based on our observations, we provide a detailed analysis of how model architecture and dataset properties affect spatio-temporal predictive learning performance. Surprisingly, we find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models. Thus, we further extend the common MetaFormers to boost recurrent-free spatial-temporal predictive learning. We open-source the code and models at https://github.com/chengtan9907/OpenSTL.Comment: Accepted by NeurIPS 2023. 33 pages, 17 figures, 19 tables. Under review. For more details, please refer to https://github.com/chengtan9907/OpenST