Postsynaptic protein organization revealed by electron microscopy.

Neuronal synapses are key devices for transmitting and processing information in the nervous system. Synaptic plasticity, generally regarded as the cellular basis of learning and memory, involves changes of subcellular structures that take place at the nanoscale. High-resolution imaging methods, especially electron microscopy (EM), have allowed for quantitative analysis of such nanoscale structures in different types of synapses. In particular, the semi-ordered organization of neurotransmitter receptors and their interacting scaffolds in the postsynaptic density have been characterized for both excitatory and inhibitory synapses by studies using various EM techniques such as immuno-EM, electron tomography of high-pressure freezing and freeze-substituted samples, and cryo electron tomography. These techniques, in combination with new correlative approaches, will further facilitate our understanding of the molecular organization underlying diverse functions of neuronal synapses

Accumulation of Dense Core Vesicles in Hippocampal Synapses Following Chronic Inactivity.

The morphology and function of neuronal synapses are regulated by neural activity, as manifested in activity-dependent synapse maturation and various forms of synaptic plasticity. Here we employed cryo-electron tomography (cryo-ET) to visualize synaptic ultrastructure in cultured hippocampal neurons and investigated changes in subcellular features in response to chronic inactivity, a paradigm often used for the induction of homeostatic synaptic plasticity. We observed a more than 2-fold increase in the mean number of dense core vesicles (DCVs) in the presynaptic compartment of excitatory synapses and an almost 20-fold increase in the number of DCVs in the presynaptic compartment of inhibitory synapses after 2 days treatment with the voltage-gated sodium channel blocker tetrodotoxin (TTX). Short-term treatment with TTX and the N-methyl-D-aspartate receptor (NMDAR) antagonist amino-5-phosphonovaleric acid (AP5) caused a 3-fold increase in the number of DCVs within 100 nm of the active zone area in excitatory synapses but had no significant effects on the overall number of DCVs. In contrast, there were very few DCVs in the postsynaptic compartments of both synapse types under all conditions. These results are consistent with a role for presynaptic DCVs in activity-dependent synapse maturation. We speculate that these accumulated DCVs can be released upon reactivation and may contribute to homeostatic metaplasticity

Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation

Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept; 2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art.Comment: Submited to IEEE TI

ARHI (DIRAS 3), an Imprinted Tumor Suppressor Gene, Binds to Importins, and Blocks Nuclear Translocation of Stat3

ARHI (DIRAS3) is an imprinted tumor suppressor gene whose expression is lost in the majority of breast and ovarian cancers. Unlike its homologs Ras and Rap, ARHI functions as a tumor suppressor. Our previous study showed that ARHI can interact with transcription activator Stat3 and inhibit its nuclear translocation in human breast and ovarian cancer cells. To identify proteins that interact with ARHI in nuclear translocation, we have performed proteomic analysis and identified several importins that can associate with ARHI. To further explore this novel finding, we have purified 10 GST-importin fusion proteins (importin 7, 8, 13, b1, a1, a3, a5, a6, a7 as well as mutant a1). Using a GST-pull down assay, we found that ARHI can bind strongly to most importins; however, its binding is significantly reduced with an importin a1 mutant which contains an altered nuclear localization signal (NLS) domain. In addition, an ARHI N-terminal deletion mutant (NTD) exhibits much less binding to all importins than does wild type ARHI ARHI and NTD proteins were purified and tested for their ability to inhibit nuclear importation of proteins in HeLa cells. ARHI protein inhibits interaction of Ran-importin complexes with GFP fusion proteins that contain an NLS domain and a beta-like import receptor binding domain, blocking their nuclear localization. Addition of ARHI also blocked nuclear localization of phosphorylated Stat3β. By GST-pull down assays, we found that ARHI could compete for Ran-importins binding. Thus, ARHI-induced disruption of importin binding to cargo proteins including Stat3 could serve as an important regulatory mechanism that contributes to the tumor suppressor function of ARHI

A Compositional Analysis of Unbalanced Usages of Multiple Left-turn Lanes

Lane usage measures distribution of a specific traffic movement across multiple available lanes in a given time. Unbalanced lane usages decrease the capacity of subject segment. This paper took multiple left-turn lanes at signalized intersections as case study, and explored the influences of some factors on the lane usage balance. Lane usages were calculated from field collected lane volumes and the constant-sum constraint among them was explicitly considered in the statistical analysis. Classical and compositional analysis of variance was respectively conducted to identify significant influential factors. By comparing the results of compositional analysis and those of the classical one, the former ones have better interpretability. It was found that left-turn lane usages could be affected by parameter variance of geometric design or traffic control, such as length of turning curve, length of upstream segment, length of signal phase or cycle. These factors could make the lane usages achieve relative balance at different factor levels.</p