Glioblastomas are highly lethal cancers for which conventional therapies provide only palliation. The cellular heterogeneity of glioblastomas is manifest in genetic and epigenetic variation with both stochastic and hierarchical models informing cellular phenotypes. At the apex of the hierarchy is a self-renewing, tumorigenic, cancer stem cell (CSC). The significance of CSCs is underscored by their resistance to cytotoxic therapies, invasive potential, and promotion of angiogenesis. Thus, targeting CSCs may offer therapeutic benefit and sensitize tumors to conventional treatment, demanding elucidation of CSC regulation. Attention has been paid to intrinsic cellular systems in CSCs, but recognition of extrinsic factors is evolving. Glioma stem cells (GSCs) are enriched in functional niches—prominently the perivascular space and hypoxic regions. These niches provide instructive cues to maintain GSCs and induce cellular plasticity towards a stem-like phenotype. GSC-maintaining niches may therefore offer novel therapeutic targets but also signal additional complexity with perhaps different pools of GSCs governed by different molecular mechanisms that must be targeted for tumor control.

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The flexible electronics has been deemed to be a promising approach to the wearable electronic systems. However, the mismatching between the existing flexible deices and the conventional computing paradigm results an impasse in this field. In this work, a new way to access to this goal is proposed by combining flexible devices and the neuromorphic architecture together. To achieve that, a high-performance flexible artificial synapse is created based on a carefully designed and optimized memristive transistor. The device exhibits high-performance which has near-linear non-volatile resistance change under 10,000 identical pulse signals within the 515% dynamic range, and has the energy consumption as low as 45 fJ per pulse. It also displays multiple synaptic plasticity features, which demonstrates its potential for real-time online learning. Besides, the adaptability by virtue of its threeterminal structure specifically contributes its improved uniformity, repeatability, and reduced power consumption. This work offers a very viable solution for the future wearable computing.

The segmentation and classification of leaves in plant images are a great challenge, especially when several leaves are overlapping in images with a complicated background. In this paper, the segmentation and classification of leaf images with a complicated background using deep learning are studied. First, more than 2500 leaf images with a complicated background are collected and artificially labeled with target pixels and background pixels. Two-thousand of them are fed into a Mask Region-based Convolutional Neural Network (Mask R-CNN) to train a model for leaf segmentation. Then, a training set that contains more than 1500 training images of 15 species is fed into a very deep convolutional network with 16 layers (VGG16) to train a model for leaf classification. The best hyperparameters for these methods are found by comparing a variety of parameter combinations. The results show that the average Misclassification Error (ME) of 80 test images using Mask R-CNN is 1.15%. The average accuracy value for the leaf classification of 150 test images using VGG16 is up to 91.5%. This indicates that these methods can be used to segment and classify the leaf image with a complicated background effectively. It could provide a reference for the phenotype analysis and automatic classification of plants.

The two-terminal synaptic device based on organic ferroelectricity with low energy consumption can provide reliable synaptic function.