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Olfactory impairment is an initial non-motor symptom of Parkinson's disease that causes the deposition of aggregated α-synuclein (α-syn) in olfactory neurons. Transient receptor potential canonical (TRPC) channels are a diverse group of non-selective Ca

In an autonomous underwater vehicles– (AUVs–) based optical-acoustic hybrid network, it is critical to achieve ultra high-speed reliable communications, in order to reap the benefits of the complementary systems and perform high-bandwidth and low-latency operations. However, as the mobile AUVs operate in harsh oceanic environments, it is essential to design an effective switching algorithm to execute flexible hybrid acoustic-optical communications and increase the network throughput. In this paper, we propose a Q-learning-based adaptive switching scheme to maximize the network throughput by capturing the dynamics of the varying channels as well as the mobility of AUVs. In order to address the challenge associated with partial observations of the optical channel and improve the switching efficiency in extreme conditions, a blind optical channel estimation method is designed and implemented with the Extended Kalman Filter (EKF), in which the relationship between the underwater acoustic and optical channels is utilized to improve the channel prediction accuracy. Based on this environmental status, a reinforcement learning approach is leveraged to build a near-optimal switching strategy for the hybrid network. We conduct numerical simulations to verify the performance of the scheme, and the simulation results demonstrate that the proposed switching scheme is effective and robust.

Mitochondrial dysfunction and oxidative stress are thought to play a dominant role in the pathogenesis of Parkinson's disease (PD). Mogroside V (MV), extracted from Siraitia grosvenorii, exhibits antioxidant-like activities. The aim of this study was to investigate the function of MV in neuroprotection in PD and to reveal its mechanism of action. To that end, we firstly set up mice models of PD with unilateral striatum injection of 0.25 mg/kg rotenone (Rot) and co-treated with 2.5 mg/kg, 5 mg/kg, and 10 mg/kg MV by gavage. Results showed that Rot-induced motor impairments and dopaminergic neuronal damage were reversed by treatment of 10 mg/kg MV. Then, we established cellular models of PD using Rot-treated SH-SY5Y cells, which were divided into six groups, including control, Rot, and co-enzyme Q10 (CQ10), as well as MV groups, MV25, MV50, and MV100 treated with 25 μM, 50 μM, and 100 μM MV doses, respectively. Results demonstrated that MV effectively attenuates Rot neurotoxicity through a ROS-related intrinsic mitochondrial pathway. MV reduced overproduction of reactive oxygen species (ROS), recovered the mitochondrial membrane potential (MMP), and increased the oxygen consumption rate and adenosine triphosphate (ATP) production in a dose-dependent manner. Hence, treatment with MV led to a reduction in the number of apoptotic cells, as reflected by Annexin-V/propidium iodide co-staining using flow cytometry and TdT-mediated dUTP Nick-End Labeling (TUNEL) assay. In addition, the Sirtuin3 (SIRT3) protein level and activity were decreased upon exposure to Rot both in substantia nigra (SN) of mice and SH-SY5Y cells. SIRT3 impairment hyperacetylated a key mitochondrial antioxidant enzyme, superoxide dismutase 2 (SOD2). MV alleviates SIRT3 and SOD2 molecular changes. However, after successfully inhibiting SIRT3 by its specific inhibitor 3-1H-1, 2, 3-triazol-4-yl pyridine (3TYP), MV was not able to reduce ROS levels, reverse abnormal MMP, or decrease apoptotic cells. Motor impairments and dopaminergic neuronal injury in the SN were alleviated with the oral administration of MV in Rot-treated PD mice, indicating a relationship between protection against defective motility and preservation of dopaminergic neurons. Therefore, we conclude that MV can alleviate Rot-induced neurotoxicity in a PD model, and that SIRT3 may be an important regulator in the protection of MV.

The etiology of Parkinson's disease (PD) is complex and the mechanism is unclear. It has become a top priority to find common factors that induce and affect PD pathology. We explored the key role of hypoxia in promoting the pathological propagation of α-synuclein (α-syn) and the progression of PD.We performed PD modeling by conducting intracranial stereotaxic surgery in the unilateral striatum of mice. We then measured protein aggregation in vitro. The rotarod and pole tests were employed next to measure the damage of the phenotype. Pathological deposition and autophagy were also observed by immunofluorescence staining and protein levels measured by western blotting.We demonstrated that short-term hypoxia activated phosphorylated (p)-α-syn in mice. We confirmed that p-α-syn was more readily formed aggregates than α-syn in vitro. Furthermore, we found that hypoxia promoted the activation and propagation of endogenous α-syn, contributing to the earlier degeneration of dopaminergic neurons in the substantia nigra and the deposition of p-α-syn in our animal model. Finally, autophagy inhibition contributed to the above pathologies.Hypoxia was shown to accelerate the pathological progression and damage phenotype in PD model mice. The results provided a promising research target for determining common interventions for PD in the future.

Despite extensive research efforts, underwater sensor networks (UWSNs) still suffer from serious performance issues due to their inefficient and uncoordinated channel access and resource management. For example, due to the lack of holistic knowledge on the network resources, existing decentralized routing protocols fail to provide globally optimal performance. On the other hand, Software Defined Networking (SDN), as a promising paradigm to provide prominent centralized solutions, can be employed to address the aforementioned issues in UWSNs. Indeed, SDN brings unprecedented opportunities to improve the network performance through the development of advanced algorithms at controllers. In this paper, we study the routing problem in such a network with new features including centralized route decision, global network-state awareness, seamless route discovery while considering the optimization of several long-term global performance metrics. We formulate the entire routing problem of a multi-modal UWSN as an optimization problem while considering the interference phenomenon of ad hoc scenarios and some long-term global performance metrics of an ideal routing protocol. Our formulated problem nicely captures all possible flexibilities of a sensor node no matter it has the full-duplex or half-duplex functionality. Upon the formulation, we recognize the NP-hard nature of the problem for all possible scenarios. We adopt a rounding technique based on the convex programming relaxation concept to solve the formulated routing problem that considers full-duplex scenarios, whereas we solve the problem for half-duplex scenarios using a greedy method upon interpreting it as a submodular function maximization problem. Through extensive simulation via our Python-based in-house simulator, we verify that our proposed globally optimal routing scheme always outperforms three existing decentralized routing protocols (each of these protocols are selected from each of three prominent protocol types, i.e., flooding, cross-layer information and adaptive machine learning based, respectively) in terms of reliability, latency, energy efficiency, lifetime and fairness.

The accumulation and transmission of α-Synuclein (α-Syn) is vital in the pathogenesis of synucleinopathies, including Parkinson’s disease (PD), dementia with Lewy body and multiple system atrophy. Clinical progression and severity of synucleinopathies are associated with deposition and spread of protein aggregates. α-Syn spreads between cells in a prion-like manner and causes neurotoxicity. However, the cellular mechanisms underlying misfolded α-Syn accumulation and propagation have not been conclusively determined. Low-density lipoprotein receptor-related protein 1 (LRP1), which is considered as a multifunctional endocytic receptor that is abundantly expressed in neurons, is elevated in the neurons of PD patients. In this study, elevated LRP1 levels were found in PC12 cells treated with extracellular added α-Syn pre-formed fibrils (PFFs) as well as PFFs-injected PD mice. Cytoplasmic α-Syn levels were markedly suppressed by LRP1 knockdown in vitro and in vivo. Additionally, LRP1 levels were elevated in cells transfected with full length and N-terminus of α-Syn. Interactions between α-Syn and LRP1 were mediated by lysine residues in the N-terminus of α-Syn. Furthermore, downregulation of LRP1 in mice effectively suppressed the propagation of α-Syn between neurons. These findings indicate that LRP1 regulates the uptake and propagation of α-Syn in the brains and may be a potential target for the treatment of α-Syn aggregation- and propagation-associated diseases.

QUasi-Affine TRansformation Evolutionary algorithm (QUATRE) is a new optimization algorithm based on population for complex multiple real parameter optimization problems in real world. In this paper, a novel multi-group multi-choice communication strategy algorithm for QUasi-Affine TRansformation Evolutionary (MM-QUATRE) algorithm is proposed to solve the disadvantage that the original QUATRE is always easily to fall into local optimization in the strategy of updating bad nodes with multiple groups and multiple choices. We compared it with other intelligent algorithms, the most advanced PSO variant, parallel PSO (P-PSO) variant, native QUATRE and parallel QUATRE (P-PSO) under CEC2013 large-scale optimization test suite. Thus, the performance of MM-QUATRE was verified. The conclusion that the MM-QUATRE algorithm is superior to other intelligent algorithms is proved by the experimental results. In addition, the application results of MM-QUATRE algorithm (MM-QUATRE-RSSI) based on RSSI in WSN node localization were analyzed and studied. The results appear that this method has higher localization accuracy than other similar algorithms.

Traditional beamforming (BF) methods can be used to suppress the Gaussian interferers in underwater acoustic communication systems. However, multiple unsteady sub-Gaussian interferers may exist that degrade the performance of BF. In this paper, a new beamformer is proposed for the suppression of multiple unsteady sub-Gaussian interferers (SMUSGI). The proposed BF combines the multiplier and the quasi-Newton methods to obtain the optimal weight vector. Simulation experiments are conducted to validate the performance of the proposed beamformer. In addition, the proposed beamformer is compared with the traditional methods including the MVDR, Subspace, Bartlett, MDDR, LCMD and LCMV beamformers. The obtained results demonstrate that the proposed beamformer can effectively suppress multiple unsteady sub-Gaussian interferers with a higher output signal-to-interference-plus-noise ratio (SINR), and faster convergence compared with the traditional methods.

Galanin receptor1 (GalR1) transcript levels are elevated in the rat ventral periaqueductal gray (vPAG) after chronic mild stress (CMS) and are related to depression-like behavior. To explore the mechanisms underlying the elevated GalR1 expression, we carried out molecular biological experiments in vitro and in animal behavioral experiments in vivo. It was found that a restricted upstream region of the GalR1 gene, from −250 to −220, harbors an E-box and plays a negative role in the GalR1 promoter activity. The transcription factor Scratch2 bound to the E-box to down-regulate GalR1 promoter activity and lower expression levels of the GalR1 gene. The expression of Scratch2 was significantly decreased in the vPAG of CMS rats. Importantly, local knockdown of Scratch2 in the vPAG caused elevated expression of GalR1 in the same region, as well as depression-like behaviors. RNAscope analysis revealed that GalR1 mRNA is expressed together with Scratch2 in both GABA and glutamate neurons. Taking these data together, our study further supports the involvement of GalR1 in mood control and suggests a role for Scratch2 as a regulator of depression-like behavior by repressing the GalR1 gene in the vPAG.

Saliency detection is a hot topic in the field of computer vision and pattern recognition, thus plenty of saliency models have been exploited and extended to various visual correspondence applications. Currently, it's still confronted with a variety of obstacles and challenges, although it has been studied for decades. With the progress of saliency detection, different computational models and salient features have been proposed and some of them improve and compensate the deficiencies of the others. In this paper, we focus on investigating the salient feature fusion strategies in human visual attention mechanism for saliency detection (e.g., linear and non-linear), in order to efficiently incorporate various salient cues for achieving a better result. Based on the complementary principle, we firstly construct a saliency map based on the information of the image background. Then, we generate a supplemental saliency map from the compactness saliency features. Finally, we evaluate the performance of six individual fusion strategies including both linear and non-linear models in terms of three publicly available image datasets. Experimental results show that our designed non-linear fusion strategy based on least-square method outperforms the other fusion strategies in saliency detection.

In underwater wireless sensor networks (UWSNs), media access control (MAC) is important because it may have a significant impact on network performance; however, the complex and changeable underwater communication environment is a great challenge for the MAC protocol. In flowing water, the network nodes are constantly moving, and the number of competitors in the network also varies. The existing hybrid MAC protocol neither can adapt to the dynamic network load nor can switch the access control protocols with changing network loads, which may result in poor network performance. In order to solve the above problems, this paper proposes a load-based time slot allocation (LBTSA) protocol. The LBTSA selects the slot allocation scheme, from a set of possible schemes, according to the instantaneous network load. Then, based on the relative priority of the nodes and the optimal number of backoff stages, the host node selects the optimal access control protocol. This not only adapts well to changing network loads but also maximizes network throughput. By assuming that the number of competitors obeys a universal Poisson distribution, the LBSTA protocol and the HCR (a hybrid MAC protocol using channel reservation) protocol are compared. As the results show, the throughput of the LBTSA is higher than that of the HCR protocol, and the end-to-end delay of the LBTSA is lower than that of the HCR protocol.

Localization has always been an essential application of underwater acoustic sensor networks (UASNs), which plays an important role in routing strategies design, node recycling, and so on. The complex ocean condition, prior infrastructure deployment, and time synchronization among beacons challenge the application of the UASN's localization. To solve these problems, we propose a novel underwater acoustic sensor networks localization algorithm based on the virtual node assistance. The algorithm is classified into two parts based on the current marine environment, including Virtual node Assisted Static (VAS) localization algorithm and Virtual node Assisted Dynamic (VAD) localization algorithm. An auxiliary node, which does not directly participate in the localization, is deployed for virtual node setup, error measurement, and RSSI ranging. The GPS-equipped ship utilizes virtual node and geometry to realize the UASN's localization without complicated deployment procedures and time synchronization. The simulation results show that our proposed algorithm can achieve the properties, including high localization coverage and small localization error as well as low communication overhead in the UASNs.

In multi-function underwater wireless sensor networks (UWSNs), multiple applications share the same physical infrastructure to fully exploit network resources. Under such scenarios, diverse packets coexist in the same network which require differentiated delivery strategies to satisfy application demands, such as ocean monitoring packets and multimedia-based intrusion detection packets (e.g., video streams, voice or images) demand versatile support for data forwarding operation. However, the limited bandwidth and high propagation delay of acoustic channel pose great challenges to satisfy such demands. A feasible solution to solve such a problem is to exploit multimodal networks which integrate complementary acoustic and non-acoustic technologies to enhance transmission capability. Therefore, in this paper, we leverage both surface wireless radio frequency (RF) and underwater acoustic technology to fulfill different performance requirements of underwater sensor networks. We first propose two multimodal acoustic-RF adaptive routing schemes, and identify the major factors which influence the performance of these adaptive protocols. Then, we conduct extensive evaluations of the algorithms for both grid and random deployment scenarios. The simulation results confirm the feasibility of the proposed multimodal acoustic-RF routing protocols under diverse communication scenarios and channels.

Underwater sensor networks would reap multiple benefits by extending the principles of software-defined networking (SDN) to it, such as programmable and reconfigurable network, enhanced network management and efficiency. However, there are several challenges need to be tackled in the SDN-enabled underwater network design process, especially for underwater mobile networks. Different from the terrestrial wireless sensor networks (WSNs), it is difficult to implement reliable centralized control as underwater wireless sensor networks (UWSNs) typically operate in the harsh ocean environment via band-limited acoustic channels. To cope with the problem, in this paper, we explore hybrid solutions and try to reap advantages of both centralized and distributed control. We proposed a semi-centralized control model for multi-modal mobile autonomous underwater vehicle (AUV) networks which are equipped with multiple communication systems (e.g., optical and acoustic). We present the initial research of the network model and the intelligent control architecture, and hope to inspire more research in the SDN-based mobile multi-modal AUV networks.

Existing range-based localization schemes for under-water acoustic sensor networks (UASNs) rely on sufficient and accurate distance measurements. However, in practice, ranging packets are inevitably corrupted due to packet collisions and signal noises, resulting in missing and noisy distance measurements and further degrading localization performance significantly. In this paper, we propose a packet corruption tolerant localization algorithm to address this challenge. First, we design an energy-efficient mechanism to gather inter-node distance measurements and form partially observed Square Distance Matrix (SDM). Then, leveraging the intrinsic low-rank structure of SDM, the reconstruction of true SDM is formulated as a Frobenius-norm regularized matrix factorization problem and an improved Newton-Raphson method is designed to solve this problem. Finally, we apply Multi-Dimension Scaling technique to localize all the nodes based on the reconstructed SDM. Simulation results demonstrate that, our proposed algorithm outperforms the benchmark approaches in terms of localization accuracy, coverage and stability.

Recently, many graph-based algorithms are applied in the research of saliency detection, which use the border of an image as a background query. This frequently leads to undesired errors and retrieval outputs when the boundaries of the salient objects concerned touch, or connect with, the image’s border. In this paper, a novel bottom-up saliency-detection algorithm is proposed to tackle and overcome the above issue. First, we utilize object proposals to collect the background seeds reliably. Then, the Extended Random Walk (ERW) algorithm is adopted to propagate the prior background labels to the rest of the pixels in an image. Finally, we refine the saliency map by taking both the textural and structure-information into consideration simultaneously. Experiments on publicly available data sets show that our proposed method achieves competitive results against the state-of-the-art approaches.

Ocean and water basically cover the major parts of our planet. To obtain the best utilization of the underlying resources on these parts of the Earth, people have made some research advancements. Specifically, the research on underwater wireless acoustic sensor networks (UWA-SNs) has made great progress. However, wide deployment of UWA-SNs is far from a reality due to several reasons. One important reason is that offshore deployment and field-level experiments of ocean-centric applications are both expensive and labor intensive. Other alternatives to attain this objective are to conduct simulation or experimentation that can reduce cost and accelerate the research activities and their outcomes. However, designing efficient and reliable simulation and experimentation platforms have proven to be more challenging beyond the expectation. In this article, we explore the main techniques (including their pros and cons) and components to develop simulation and experimentation platforms and provide a comprehensive survey report in this area. We classify simulation and experimentation platforms based on some typical criteria and then provide useful guidelines for researchers on choosing suitable platforms in accordance with their requirements. Finally, we address some open and un-resolved issues in this context and provide some suggestions on future research.

ETS-1 is a transcription factor that is a member of the E26 transformation-specific (ETS) family. Galanin receptor 2 (GalR2), a subtype of receptors of the neuropeptide galanin, has been shown to have an antidepressant-like effect after activation in rodents. Our previous study has shown that overexpression of ETS-1 increases the expression of GalR2 in PC12 phaeochromocytoma cells. However, whether ETS-1 has an antidepressant-like effect is still unclear. In this study, we found that chronic mild stress (CMS) decreased the expression of both ETS-1 and GalR2 in the ventral hippocampus of rats. Meanwhile, we demonstrated that overexpression of ETS-1 increased the expression of GalR2 in primary hippocampal neurons. Importantly, we showed that overexpression of ETS-1 in the ventral hippocampus counteracted the depression-like behaviors of CMS rats. Furthermore, we found that overexpression of ETS-1 increased the level of downstream phosphorylated extracellular signal-regulated protein kinases 1 and 2 (p-ERK1/2) of GalR2 in the ventral hippocampus of CMS rats. Taken together, our findings suggest that ETS-1 has an antidepressant-like effect in rats, which might be mediated by increasing the level of GalR2 and its downstream p-ERK1/2 in the ventral hippocampus. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12264-019-00412-6) contains supplementary material, which is available to authorized users.

With increasing attentions being drawn to the underwater observation and utilization of marine resources in recent years, underwater image processing and analysis have become an active research hotspot. Different from the general images, marine environment is usually faced with some complicated situations such as underwater turbulence and diffusion, severe absorption and scattering of water body, various noises, low contrast, uniform illumination, monotonous color, complex underwater-background. In response to these typical challenges, a large body of works in underwater image processing has been exploited in recent years. This survey introduces a review of existing relatively mature and representative underwater image processing models, which are classified into seven categories including enhancement, fog removal, noise reduction, segmentation, salient object detection, color constancy and restoration. We then objectively evaluate the current situations and future development trend of underwater image processing, and provide some insights into the prospective research directions to promote the development of underwater vision and beyond.