Reasoning human object interactions is a core problem in human-centric scene understanding and detecting such relations poses a unique challenge to vision systems due to large variations in human-object configurations, multiple co-occurring relation instances and subtle visual difference between relation categories. To address those challenges, we propose a multi-level relation detection strategy that utilizes human pose cues to capture global spatial configurations of relations and as an attention mechanism to dynamically zoom into relevant regions at human part level. Specifically, we develop a multi-branch deep network to learn a pose-augmented relation representation at three semantic levels, incorporating interaction context, object features and detailed semantic part cues. As a result, our approach is capable of generating robust predictions on fine-grained human object interactions with interpretable outputs. Extensive experimental evaluations on public benchmarks show that our model outperforms prior methods by a considerable margin, demonstrating its efficacy in handling complex scenes. Code is available at https://github.com/bobwan1995/PMFNet.

Artificial muscles possess a vast potential in accelerating the development of robotics, exoskeletons, and prosthetics. Although a variety of emerging actuator technologies are reported, they suffer from several issues, such as high driving voltages, large hysteresis, and water intolerance. Here, a liquid metal artificial muscle (LMAM) is demonstrated, based on the electrochemically tunable interfacial tension of liquid metal to mimic the contraction and extension of muscles. The LMAM can work in different solutions with a wide range of pH (0–14), generating actuation strains of up to 87% at a maximum extension speed of 15 mm s−1. More importantly, the LMAM only needs a very low driving voltage of 0.5 V. The actuating components of the LMAM are completely built from liquids, which avoids mechanical fatigue and provides actuator linkages without mechanical constraints to movement. The LMAM is used for developing several proof‐of‐concept applications, including controlled displays, cargo deliveries, and reconfigurable optical reflectors. The simplicity, versatility, and efficiency of the LMAM are further demonstrated by using it to actuate the caudal fin of an untethered bionic robotic fish. The presented LMAM has the potential to extend the performance space of soft actuators for applications from engineering fields to biomedical applications.

No abstract

Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

Our previous studies revealed RBM8A may play a role in various progressive neurological diseases. The present study aimed to explore the role of RBM8A in Alzheimer's disease (AD). RBM8A is significantly down-regulated in AD. Interestingly, 9186 differentially expressed genes are overlapped from comparisons of AD versus control and RBM8A-low versus RBM8A-high. Weight gene correlation analysis was performed and 9 functional modules were identified. Modules positively correlated with AD and RBM8A-low are significantly involved in the RAP1 signaling pathway, PI3K−AKT signaling pathway, hematopoietic cell lineage, autophagy and APELIN signaling pathway. Fifteen genes (RBM8A, RHBDF2, TNFRSF10B, ACP1, ANKRD39, CA10, CAMK4, CBLN4, LOC284214, NOVA1, PAK1, PPEF1, RGS4, TCEB1 and TMEM118) are identified as hub genes, and the hub gene-based LASSO model can accurately predict the occurrence of AD (AUC = 0.948). Moreover, the RBM8A-module-pathway network was constructed, and low expression of RBM8A down-regulates multiple module genes, including FIP200, Beclin 1, NRBF2, VPS15 and ATG12, which composes key complexes of autophagy. Thus, our study supports that low expression of RBM8A correlates with the decrease of the components of key complexes in autophagy, which could potentially contribute to pathophysiological changes of AD.

No abstract

Multidrug resistance (MDR) is a serious problem during cancer therapy. The purpose of the present study was to formulate D-α-Tocopheryl polyethylene glycol 1000 succinate-resveratrol-solid lipid nanoparticles (TPGS-Res-SLNs) to improve its therapeutic efficacy against breast cancer. In this study, the solvent injection method was used to prepare the TPGS-Res-SLNs. It was found that the TPGS-Res-SLNs exhibited zeta potential and drug-loading of −25.6 ± 1.3 mV and 32.4 ± 2.6%, respectively. Therefore, it was evident that the TPGS-Res-SLNs can increase cellular uptake of chemotherapeutic drugs, induce mitochondrial dysfunction, and augment tumor treatment efficiency by inducing apoptosis. Moreover, it was found that SKBR3/PR cells treated with TPGS-Res-SLNs exhibited significant inhibition of cell migration and invasion, as compared with free resveratrol. In addition, results from in vivo SKBR3/PR xenograft tumor models revealed that TPGS-Res-SLNs has better efficacy in promoting apoptosis of tumor cells owing to high therapeutic outcomes on tumors when compared with the efficacy of free resveratrol. In conclusion, the findings of the present study indicate significant potential for use of TPGS-Res-SLNs as an efficient drug delivery vehicle to overcome drug resistance in breast cancer therapy.

Competing endogenous RNA (ceRNA) and autophagy were related to neurological diseases. But the relationship among ceRNA, autophagy and Schizophrenia (SZ) was not clear. In this study, we obtained gene expression profile of SZ patients (GSE38484, GSE54578, and GSE16930) from Gene Expression Omnibus (GEO) database. Then we screened the autophagy-related differentially expressed lncRNA, miRNA, and mRNA (DElncRNA, DEmiRNA, and DEmRNA) combined with Gene database from The National Center for Biotechnology Information (NCBI). In addition, we performed enrichment analysis. The result showed that biological processes (BPs) mainly were associated with cellular responses to oxygen concentration. The enriched pathways mainly included ErbB, AMPK, mTOR signaling pathway and cell cycle. Furthermore, we constructed autophagy-related ceRNA network based on the TargetScan database. Moreover, we explored the diagnostic efficiency of lncRNA, miRNA and mRNA in ceRNA, through gene set variation analysis (GSVA). The result showed that the diagnostic efficiency was robust, especially miRNA (AUC = 0.884). The miRNA included hsa-miR-423-5p, hsa-miR-4532, hsa-miR-593-3p, hsa-miR-618, hsa-miR-4723-3p, hsa-miR-4640-3p, hsa-miR-296-5p, and hsa-miR-3943. The result of this study may be helpful for deepening the pathophysiology of SZ. In addition, our finding may provide a guideline for the clinical diagnosis of SZ.