Design, Synthesis, and Biological Evaluation of Eukaryotic Initiation Factor 2B (eIF2B) Activators.

The eukaryotic initiation factor 2B (eIF2B) is a key regulator in protein-regulated signaling pathways and is closely related to the function of the central nervous system. Modulating eIF2B could retard the process of neurodegenerative diseases, including Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and vanishing white matter disease (VWM) et al. Here, we designed and synthesized a series of novel eIF2B activators containing oxadiazole fragments. The activating effects of compounds on eIF2B were investigated through testing the inhibition of ATF4 expression. Of all the targeted compounds, compounds 21 and 29 exhibited potent inhibition on ATF4 expression with IC50 values of 32.43 nM and 47.71 nM, respectively, which were stronger than that of ISRIB (IC50=67.90 nM). ATF4 mRNA assay showed that these two compounds could restore ATF4 mRNA to normal levels in thapsigargin-stimulated HeLa cells. Protein Translation assay showed that both compounds were effective in restoring protein synthesis. Compound potency assay showed that both compounds had similar potency to ISRIB with EC50 values of 5.844 and 37.70 nM. Cytotoxicity assay revealed that compounds 21 and 29 had low toxicity and were worth further investigation.

Chemogenetic activation of astrocytic Gi signaling promotes spinogenesis and motor functional recovery after stroke.

Ischemic stroke is the leading cause of adult disability. The rewiring of surviving neurons is the fundamental process for functional recovery. Accumulating evidence implicates astrocytes in synapses and neural circuits formation, but few studies have further studied how to enhance the effects of astrocytes on synapse and circuits after stroke and its impacts on post-stroke functional recovery. In this study, we made use of chemogenetics to specifically activate astrocytic Gi signaling in the peri-infarcted sensorimotor cortex at different time epochs in a mouse model of photothrombotic stroke. We found that early activation of astrocytic hM4Di after stroke by CNO modulates astrocyte activity and upregulates synaptogenic molecules including thrombospondin-1 (TSP1) as revealed by bulk RNA-sequencing, but no significant improvement was observed in dendritic spine density and behavioral performance in grid walking test. Interestingly, when the manipulation was initiated at the subacute phase of stroke, the recovery of spine density and motor function could be effectively promoted, accompanied by increased TSP1 expression. Our data highlight the important role of astrocytes in synapse remodeling during the repair phase of stroke and suggest astrocytic Gi signaling activation as a potential strategy for synapse regeneration, circuit rewiring, and functional recovery.

Collective Reconstructive Embeddings for Cross-modal Hashing.

In this paper, we study the problem of cross-modal retrieval by hashing-based approximate nearest neighbor (ANN) search techniques. Most existing cross-modal hashing work mainly addresses the issue of multi-modal integration complexity using the same mapping and similarity calculation for data from different media types. Nonetheless, this may cause information loss during the mapping process due to overlooking the specifics of each individual modality. In this work, we propose a simple yet effective cross-modal hashing approach, termed Collective Reconstructive Embeddings (CRE), which can simultaneously solve the heterogeneity and integration complexity of multi-modal data. To address the heterogeneity challenge, we propose to process heterogeneous types of data using different modalityspecific models. Specifically, we model textual data with cosine similarity based reconstructive embedding to alleviate the data sparsity to the greatest extent, while for image data we utilize the Euclidean distance to characterize the relationships of the projected hash codes. Meanwhile, we unify the projections of text and image to the Hamming space into a common reconstructive embedding through rigid mathematical reformulation, which not only reduces the optimization complexity significantly but also facilitates the inter-modal similarity preservation among different modalities. We further incorporate the code balance and uncorrelation criteria into the problem, and devise an efficient iterative algorithm for optimization. Comprehensive experiments on four widely-used multimodal benchmarks show that the proposed CRE can achieve superior performance compared to the state-of-the-arts on several challenging cross-modal tasks.

Robust Web Image Annotation via Exploring Multi-Facet and Structural Knowledge.

Driven by the rapid development of Internet and digital technologies, we have witnessed the explosive growth of Web images in recent years. Seeing that labels can reflect the semantic contents of the images, automatic image annotation, which can further facilitate the procedure of image semantic indexing, retrieval, and other image management tasks, has become one of the most crucial research directions in multimedia. Most of the existing annotation methods, heavily rely on well-labeled training data (expensive to collect) and/or single view of visual features (insufficient representative power). In this paper, inspired by the promising advance of feature engineering (e.g., CNN feature and scale-invariant feature transform feature) and inexhaustible image data (associated with noisy and incomplete labels) on the Web, we propose an effective and robust scheme, termed robust multi-view semi-supervised learning (RMSL), for facilitating image annotation task. Specifically, we exploit both labeled images and unlabeled images to uncover the intrinsic data structural information. Meanwhile, to comprehensively describe an individual datum, we take advantage of the correlated and complemental information derived from multiple facets of image data (i.e., multiple views or features). We devise a robust pairwise constraint on outcomes of different views to achieve annotation consistency. Furthermore, we integrate a robust classifier learning component via l2,p loss, which can provide effective noise identification power during the learning process. Finally, we devise an efficient iterative algorithm to solve the optimization problem in RMSL. We conduct comprehensive experiments on three different data sets, and the results illustrate that our proposed approach is promising for automatic image annotation.