Rationale: Endothelial cell–specific molecule 1 (Esm1) is a secreted protein thought to play a role in angiogenesis and inflammation. However, there is currently no direct in vivo evidence supporting a function of Esm1 in either of these processes. Objective: To determine the role of Esm1 in vivo and the underlying molecular mechanisms. Methods and Results: We generated and analyzed Esm1 knockout ( Esm1 KO ) mice to study its role in angiogenesis and inflammation. Esm1 expression is induced by the vascular endothelial growth factor A (VEGF-A) in endothelial tip cells of the mouse retina. Esm1 KO mice showed delayed vascular outgrowth and reduced filopodia extension, which are both VEGF-A–dependent processes. Impairment of Esm1 function led to a decrease in phosphorylated Erk1/2 (extracellular-signal regulated kinases 1/2) in sprouting vessels. We also found that Esm1 KO mice displayed a 40% decrease in leukocyte transmigration. Moreover, VEGF-induced vascular permeability was decreased by 30% in Esm1 KO mice and specifically on stimulation with VEGF-A 165 but not VEGF-A 121 . Accordingly, cerebral edema attributable to ischemic stroke–induced vascular permeability was reduced by 50% in the absence of Esm1. Mechanistically, we show that Esm1 binds directly to fibronectin and thereby displaces fibronectin-bound VEGF-A 165 leading to increased bioavailability of VEGF-A 165 and subsequently enhanced levels of VEGF-A signaling. Conclusions: Esm1 is simultaneously a target and modulator of VEGF signaling in endothelial cells, playing a role in angiogenesis, inflammation, and vascular permeability, which might be of potential interest for therapeutic applications.
Recently a number of algorithms under the theme of 'unbiased learning-to-rank' have been proposed, which can reduce position bias, the major type of bias in click data, and train a highperformance ranker with click data in learning-to-rank. Most of the existing algorithms, based on the inverse propensity weighting (IPW) principle, first estimate the click bias at each position, and then train an unbiased ranker with the estimated biases using a learning-to-rank algorithm. However, there has not been a method for unbiased pairwise learning-to-rank that can simultaneously conduct debiasing of click data and training of a ranker using a pairwise loss function. In this paper, we propose a novel framework to accomplish the goal and apply this framework to the state-ofthe-art pairwise learning-to-rank algorithm, LambdaMART. Our algorithm named Unbiased LambdaMART can jointly estimate the biases at click positions and the biases at unclick positions, and learn an unbiased ranker. Experiments on benchmark data show that Unbiased LambdaMART can significantly outperform existing algorithms by large margins. In addition, an online A/B Testing at a commercial search engine shows that Unbiased LambdaMART can effectively conduct debiasing of click data and enhance relevance ranking.
Background:Foxp3+ regulatory T (Treg) cells and M2 macrophages are associated with increased tumour progression. However, the interaction between Treg cells and M2 macrophages remains unclear.Methods:The expression of FoxP3 and CD163 was detected by immunohistochemistry in 65 cases of laryngeal squamous cell carcinoma (LSCC). In vitro, the generation of activated Treg (aTreg) cells and M2 macrophages by interactions with their precursor cells were analysed by flow cytometry and ELISA. In vivo, the antitumour effects were assessed by combined targeting aTreg cells and M2 macrophages, and intratumoural immunocytes were analysed by flow cytometry.Results:In LSCC tissue, accumulation of aTreg cells and M2 macrophages predicted a poor prognosis and were positively associated with each other. In vitro, aTreg cells were induced from CD4+CD25− T cells by cancer cell-activated M2-like macrophages. Consequently, these aTreg cells skewed the differentiation of monocytes towards an M2-like phenotype, thereby forming a positive-feedback loop. Combined targeting aTreg cells and M2 macrophages led to potent antitumour immunity in vivo.Conclusions:The positive-feedback loop between aTreg cells and M2 macrophages is essential to maintain or promote immunosuppression in the tumour microenvironment and may be a potential therapeutic target to inhibit tumour progression.
The treatment of intramedullary infections after nailing usually includes removal of the nail, debridement, and, in some cases, insertion of antibiotic-impregnated cement beads. We use this self-made antibiotic cement rod to treat intramedullary infections. Compared with the beads, it provides some limited mechanical support and can be preserved in the canal for a long time. We reviewed 19 infected patients who underwent removal of the nails, excision of sinus tracks, debridement of the canal and insertion of the rods. No recurrent infection occurred in 18 cases and 11 cases achieved bone healing, 6 cases achieved partial union. One patient had nonunion and one patient underwent amputation because of severe primary trauma and long-term infection. The rod was removed between 35 and 123 days after implantation. We conclude that the antibiotic cement rods could be a relatively effective, simple and inexpensive method of treating intramedullary infections after nailing.
Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable operations for Internet of things (IoT) systems. In this paper, we study the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. First, to handle the access control problem assuming causal battery and channel state information, we propose a scheduling algorithm that maximizes the uplink transmission sum rate based on reinforcement learning (RL) with deep Q-network (DQN) enhancement. Second, for the battery prediction problem, with a fixed round-robin access control policy adopted, we develop a RL based algorithm to minimize the prediction loss (error) without any model knowledge about the energy source and energy arrival process. Finally, the joint access control and battery prediction problem is investigated, where we propose a two-layer RL network to simultaneously deal with maximizing the sum rate and minimizing the prediction loss: the first layer is for battery prediction, the second layer generates the access policy based on the output from the first layer. Experiment results show that the three proposed RL algorithms can achieve better performances compared with existing benchmarks.
Wireless sensor networks (WSNs) are effective for locating and tracking people and objects in various industrial environments. Since energy consumption is critical to prolonging the lifespan of WSNs, we propose an energy-efficient LOcalization and Tracking (eLOT) system, using low-cost and portable hardware to enable highly accurate tracking of targets. Various fingerprint-based approaches for localization and tracking are implemented in eLOT. In order to achieve high energy efficiency, a network-level scheme coordinating collision and interference is proposed. On the other hand, based on the location information, mobile devices in eLOT can quickly associate with the specific channel in a given area, while saving energy through avoiding unnecessary transmission. Finally, a platform based on TI C-C2530 and the Linux operating system is built to demonstrate the effectiveness of our proposed scheme in terms of localization accuracy and energy efficiency.
In recent years, wireless communication systems are expected to achieve more cost-efficient and sustainable operations by replacing conventional fixed power supplies such as batteries with energy harvesting (EH) devices, which could provide electric energy from renewable energy sources (e.g., solar and wind). Such EH power supplies, however, are random and instable in nature, and as a result impose new challenges on reliable communication design and have triggered substantial research interests in EH based wireless communications. Building upon existing works, in this article, we develop a general optimization framework to maximize the utility of EH wireless communication systems. Our framework encapsulates a variety of design problems, such as throughput maximization and outage probability minimization in single-user and multiuser setups, and provides useful guidelines to the practical design of general EH based communication systems with different assumptions over the knowledge of time-varying wireless channels and EH rates at the transmitters.
BackgroundAs the most common neurodegenerative disease, Alzheimer’s disease (AD) leads to progressive loss of cognition and memory. Presently, the underlying pathogenic genes of AD patients remain elusive, and effective disease-modifying therapy is not available. This study explored novel biomarkers that can affect diagnosis and treatment in AD based on immune infiltration.MethodsThe gene expression profiles of 139 AD cases and 134 normal controls were obtained from the NCBI GEO public database. We applied the computational method CIBERSORT to bulk gene expression profiles of AD to quantify 22 subsets of immune cells. Besides, based on the use of the Least Absolute Shrinkage Selection Operator (LASSO), this study also applied SVM-RFE analysis to screen key genes. GO-based semantic similarity and logistic regression model analyses were applied to explore hub genes further.ResultsThere was a remarkable significance in the infiltration of immune cells between the subgroups. The proportions for monocytes, M0 macrophages, and dendritic cells in the AD group were significantly higher than those in the normal group, while the proportion of some cells was lower than that of the normal group, such as NK cell resting, T-cell CD4 naive, T-cell CD4 memory activation, and eosinophils. Additionally, seven genes (ABCA2, CREBRF, CD72, CETN2, KCNG1, NDUFA2, and RPL36AL) were identified as hub genes. Then we performed the analysis of immune factor correlation, gene set enrichment analysis (GSEA), and GO based on seven hub genes. The AUC of ROC prediction model in test and validation sets were 0.845 and 0.839, respectively. Eventually, the mRNA expression analysis of ABCA2, NDUFA2, CREBRF, and CD72 revealed significant differences among the seven hub genes and then was confirmed by RT-PCR.ConclusionA model based on immune cell infiltration might be used to forecast AD patients’ diagnosis, and it provided a new perspective for AD treatment targets.
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