Transplantation of Ciliary Neurotrophic Factor-Expressing Adult Oligodendrocyte Precursor Cells Promotes Remyelination and Functional Recovery after SpinalCord Injury

Demyelination contributes to the dysfunction after traumatic spinal cord injury (SCI). We explored whether the combination of neurotrophic factors and transplantation of adult rat spinal cord oligodendrocyte precursor cells (OPCs) could enhance remyelination and functional recovery after SCI. Ciliary neurotrophic factor (CNTF) was the most effective neurotrophic factor to promote oligodendrocyte (OL) differentiation and survival of OPCs in vitro. OPCs were infected with retroviruses expressing enhanced green fluorescent protein (EGFP) or CNTF and transplanted into the contused adult thoracic spinal cord 9 d after injury. Seven weeks after transplantation, the grafted OPCs survived and integrated into the injured spinal cord. The survival of grafted CNTF-OPCs increased fourfold compared with EGFP-OPCs. The grafted OPCs differentiated into adenomatus polyposis coli (APC+) OLs, and CNTF significantly increased the percentage of APC+ OLs from grafted OPCs. Immunofluorescent and immunoelectron microscopic analyses showed that the grafted OPCs formed central myelin sheaths around the axons in the injured spinal cord. The number of OL-remyelinated axons in ventrolateral funiculus (VLF) or lateral funiculus (LF) at the injured epicenter was significantly increased in animals that received CNTF-OPC grafts compared with all other groups. Importantly, 75% of rats receiving CNTF-OPC grafts recovered transcranial magnetic motor-evoked potential and magnetic interenlargement reflex responses, indicating that conduction through the demyelinated axons in VLF or LF, respectively, was partially restored. More importantly, recovery of hindlimb locomotor function was significantly enhanced in animals receiving grafts of CNTF-OPCs. Thus, combined treatment with OPC grafts expressing CNTF can enhance remyelination and facilitate functional recovery after traumatic SCI

Optimal Status Update for Caching Enabled IoT Networks: A Dueling Deep R-Network Approach

In the Internet of Things (IoT) networks, caching is a promising technique to alleviate energy consumption of sensors by responding to users' data requests with the data packets cached in the edge caching node (ECN). However, without an efficient status update strategy, the information obtained by users may be stale, which in return would inevitably deteriorate the accuracy and reliability of derived decisions for real-time applications. In this paper, we focus on striking the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status. Particularly, we first depict the evolutions of the AoI with each sensor from different users' perspective with time steps of non-uniform duration, which are determined by both the users' data requests and the ECN's status update decision. Then, we formulate a non-uniform time step based dynamic status update optimization problem to minimize the long-term average cost, jointly considering the average AoI and energy consumption. To this end, a Markov Decision Process is formulated and further, a dueling deep R-network based dynamic status update algorithm is devised by combining dueling deep Q-network and tabular R-learning, with which challenges from the curse of dimensionality and unknown of the environmental dynamics can be addressed. Finally, extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with five baseline deep reinforcement learning algorithms and policies

No bursts detected from FRB121102 in two 5-hour observing campaigns with the Robert C. Byrd Green Bank Telescope

Here, we report non-detection of radio bursts from Fast Radio Burst FRB 121102 during two 5-hour observation sessions on the Robert C. Byrd 100-m Green Bank Telescope in West Virginia, USA, on December 11, 2017, and January 12, 2018. In addition, we report non-detection during an abutting 10-hour observation with the Kunming 40-m telescope in China, which commenced UTC 10:00 January 12, 2018. These are among the longest published contiguous observations of FRB 121102, and support the notion that FRB 121102 bursts are episodic. These observations were part of a simultaneous optical and radio monitoring campaign with the the Caltech HIgh- speed Multi-color CamERA (CHIMERA) instrument on the Hale 5.1-m telescope.Comment: 1 table, Submitted to RN of AA

Optimal Status Updates for Minimizing Age of Correlated Information in IoT Networks with Energy Harvesting Sensors

Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In this paper, we consider an IoT network consisting of sensors equipped with the energy harvesting (EH) capability. We optimize the average AoCI at the data fusion center (DFC) by appropriately managing the energy harvested by sensors, whose true battery states are unobservable during the decision-making process. Particularly, we first formulate the dynamic status update procedure as a partially observable Markov decision process (POMDP), where the environmental dynamics are unknown to the DFC. In order to address the challenges arising from the causality of energy usage, unknown environmental dynamics, unobservability of sensors'true battery states, and large-scale discrete action space, we devise a deep reinforcement learning (DRL)-based dynamic status update algorithm. The algorithm leverages the advantages of the soft actor-critic and long short-term memory techniques. Meanwhile, it incorporates our proposed action decomposition and mapping mechanism. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with available DRL algorithms for POMDPs

Role of Extracellular RNA and TLR3‐Trif Signaling in Myocardial Ischemia–Reperfusion Injury

Background: Toll‐like receptor 3 (TLR3) was originally identified as the receptor for viral RNA and represents a major host antiviral defense mechanism. TLR3 may also recognize extracellular RNA (exRNA) released from injured tissues under certain stress conditions. However, a role for exRNA and TLR3 in the pathogenesis of myocardial ischemic injury has not been tested. This study examined the role of exRNA and TLR3 signaling in myocardial infarction (MI), apoptosis, inflammation, and cardiac dysfunction during ischemia‐reperfusion (I/R) injury. Methods and Results: Wild‐type (WT), TLR3−/−, Trif−/−, and interferon (IFN) α/β receptor‐1 deficient (IFNAR1−/−) mice were subjected to 45 minutes of coronary artery occlusion and 24 hours of reperfusion. Compared with WT, TLR3−/− or Trif−/− mice had smaller MI and better preserved cardiac function. Surprisingly, unlike TLR(2/4)‐MyD88 signaling, lack of TLR3‐Trif signaling had no impact on myocardial cytokines or neutrophil recruitment after I/R, but myocardial apoptosis was significantly attenuated in Trif−/− mice. Deletion of the downstream IFNAR1 had no effect on infarct size. Importantly, hypoxia and I/R led to release of RNA including microRNA from injured cardiomyocytes and ischemic heart, respectively. Necrotic cardiomyocytes induced a robust and dose‐dependent cytokine response in cultured cardiomyocytes, which was markedly reduced by RNase but not DNase, and partially blocked in TLR3‐deficient cardiomyocytes. In vivo, RNase administration reduced serum RNA level, attenuated myocardial cytokine production, leukocytes infiltration and apoptosis, and conferred cardiac protection against I/R injury. Conclusion: TLR3‐Trif signaling represents an injurious pathway during I/R. Extracellular RNA released during I/R may contribute to myocardial inflammation and infarction