A combined neuro fuzzy-cellular automata based material model for finite element simulation of plane strain compression

This paper presents a modelling strategy that combines Neuro-Fuzzy methods to define the material model with Cellular Automata representations of the microstructure, all embedded within a Finite Element solver that can deal with the large deformations of metal processing technology. We use the acronym nf-CAFE as a label for the method. The need for such an approach arises from the twin demands of computational speed for quick solutions for efficient material characterisation by incorporating metallurgical knowledge for material design models and subsequent process control. In this strategy, the cellular automata hold the microstructural features in terms of sub-grain size and dislocation density which are modelled by a neuro-fuzzy system that predicts the flow stress. The proposed methodology is validated on a two dimensional (2D) plane strain compression finite element simulation with Al-1% Mg alloy. Results from the simulations show the potential of the model for incorporating the effects of the underlying microstructure on the evolving flow stress fields. In doing this, the paper highlights the importance of understanding the local transition rules that affect the global behaviour during deformation

In vitro expanded human CD4+CD25+ regulatory T cells suppress effector T cell proliferation.

Regulatory T cells (Tregs) have been shown to be critical in the balance between autoimmunity and tolerance and have been implicated in several human autoimmune diseases. However, the small number of Tregs in peripheral blood limits their therapeutic potential. Therefore, we developed a protocol that would allow for the expansion of Tregs while retaining their suppressive activity. We isolated CD4+CD25 hi cells from human peripheral blood and expanded them in vitro in the presence of anti-CD3 and anti-CD28 magnetic Xcyte Dynabeads and high concentrations of exogenous Interleukin (IL)-2. Tregs were effectively expanded up to 200-fold while maintaining surface expression of CD25 and other markers of Tregs: CD62L, HLA-DR, CCR6, and FOXP3. The expanded Tregs suppressed proliferation and cytokine secretion of responder PBMCs in co-cultures stimulated with anti-CD3 or alloantigen. Treg expansion is a critical first step before consideration of Tregs as a therapeutic intervention in patients with autoimmune or graft-versus-host disease

Imbalanced Deep Learning by Minority Class Incremental Rectification

Model learning from class imbalanced training data is a long-standing and significant challenge for machine learning. In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data. To address this problem, we formulate a class imbalanced deep learning model based on batch-wise incremental minority (sparsely sampled) class rectification by hard sample mining in majority (frequently sampled) classes during model training. This model is designed to minimise the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process. To that end, we introduce a Class Rectification Loss (CRL) function that can be deployed readily in deep network architectures. Extensive experimental evaluations are conducted on three imbalanced person attribute benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object category benchmark dataset (CIFAR-100). These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.Comment: Accepted for IEEE Trans. Pattern Analysis and Machine Intelligenc