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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.

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.

The software-defined networking paradigm enables wireless sensor networks as a programmable and reconfigurable network to improve network management and efficiency. However, several challenges arise when implementing the concept of software-defined networking in maritime wireless sensor networks, as the networks operate in harsh ocean environments, and the dominant underwater acoustic systems are with limited bandwidth and high latency, which render the implementation of software-defined networking central-control difficult. To cope with the problems and meet demand for high-speed data transmission, we propose a radio frequency–acoustic software-defined networking-based multi-modal wireless sensor network which leverages benefits of both radio frequency and acoustic communication systems for ocean monitoring. We first present the software-defined networking-based multi-modal network architecture, and then explore two examples of applications with this architecture: network deployment and coverage for intrusion detection with both grid-based and random deployment scenarios, and a novel underwater testbed design by incorporating radio frequency–acoustic multi-modal techniques to facilitate marine sensor network experiments. Finally, we evaluate the performance of deployment and coverage of software-defined networking-based multi-modal wireless sensor network through simulations with several scenarios to verify the effectiveness of the network.

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.

Encouraged by the success in both terrestrial wired and wireless networks, software-defined networking (SDN) is envisioned as a promising technique to tackle inherent problems in underwater wireless sensor networks (UWSNs), and propel the development of UWSNs further. As the offshore application deployments of UWSNs are both expensive and labor-intensive, it is urgent to develop efficient experimental testbeds or platforms in order to test or evaluate SDN-based algorithms. In this paper, we propose a novel experimental testbed for SDN-based UWSNs, in which the implementation of the control plane is with one-hop out-of-band RF channel, e.g., LoRa or LoRaWAN, whereas the traditional underwater acoustic channel serves as the data plane. Armed with the global view of the whole testbed, the two-channel SDN-based architecture not only improves the testbed efficiency, but also increases the testbed flexibility and adaptability. We provide a use case to illustrate how to use the testbed by comparing three different routing control models, such as SDN-based centralized control, SDN-based semi-centralized control and distributed control, and hope to inspire more research with this novel testbed.

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.

Galanin receptor 2 (GAL2R) is a G protein-coupled receptor for the neuropeptide galanin that regulates many important physiological functions and pathological processes. To investigate the molecular mechanism governing GAL2R gene transcription, the rat GAL2R promoter was isolated and analyzed. We found that the region from -320 to -300 of the GAL2R promoter contains two putative ETS-1 elements and plays an important role in regulating GAL2R promoter activity. We also showed that transcription factor ETS-1 bound to this region in vitro and in vivo. Overexpression of ETS-1 significantly increased GAL2R promoter activity and transcription of the GAL2R gene, whereas knockdown of ETS-1 produced the opposite effects. In addition, we showed that ETS-1 recruited co-activator p300 to the GAL2R promoter. These data indicate a role for ETS-1 in the control of the GAL2R gene expression and provide a basis for understanding the transcriptional regulation of the GAL2R gene.

Underwater sensor networks have many applications ranging from ocean monitoring, undersea exploration, target tracking, coastal surveillance, to disaster prevention. In multi-application scenarios, the network might need to handle different types of packets to satisfy the requirements of diverse data transmission metric. For example, multimedia-based applications may include different multimedia packets, such as voice, compressed images, even video streams with different quality of experience. To meet the requirements of such applications, in this paper, we propose a hybrid adaptive routing scheme (HARS) for drifting restricted floating ocean sensor networks (DR-OSNs), which exploits both surface wireless and underwater communication channels to fulfill different performance requirements. We evaluate the performance of the routing scheme and investigate the factors which affect the scheme. The simulation results demonstrate that the scheme achieves a reasonable performance for different communication channels and packet delivery.

MicroRNAs (miRNAs) play an important role in multiple biological processes, and many miRNAs have been shown to regulate cell proliferation and apoptosis. In this study, we investigated the role of miR-15b-5p in cell proliferation and apoptosis in PC12 cells. We found that overexpression of miR-15b-5p could decrease cell proliferation and induce apoptosis and cytotoxic activities in PC12 cells. Bioinformatics analysis and luciferase activities assays showed that miR-15b-5p might target extracellular signal-regulated kinase 1 (ERK1) by binding to its 3'-untranslated region (3'-UTR). Moreover, we also found that overexpression of ERK1 could attenuate the effects of miR-15b-5p in PC12 cells. Finally, our results suggest that miR-15b-5p might inhibit cell proliferation and induce apoptosis in PC12 cells by targeting ERK1.

Ocean sensor networks have many applications ranging from oceanographic data collection, pollution monitoring, disasters prevention to tactical surveillance. However, due to the harsh ocean environment, it is difficult to deploy three-dimensional sensor networks underwater for sustainable monitoring tasks. In this paper, we propose a novel floating three-dimensional sensor networks for ocean monitoring and surveillance applications, which leverage nodes' restricted movement to enlarge the monitoring area. The networks are deployed on both the surface of the ocean and underwater, with many chains of nodes attached to mooring lines. Compared with deployments requiring self-adjusted nodes, our scheme is simpler and more cost-efficient, and the sustainable monitoring cost is low. We design deployment strategies, and provide in-depth mathematical analysis for network coverage. Then we derive the minimum number of sensors to achieve full surface coverage. Evaluated by simulations of a few underwater scenarios, the effectiveness and efficiency of the proposed scheme is proved.

Localization is a critical issue for Underwater Acoustic Sensor Networks (UASNs). Existing localization algorithms mainly focus on localizing unknown nodes (location-unaware) by measuring their distances to beacon nodes (location-aware), whereas ignoring additional challenges posed by harsh underwater environments. Especially, underwater nodes move constantly with ocean currents and measurement noises vary with distances. In this paper, we consider a special drifting-restricted UASN and propose a novel beacon-free algorithm, called MAP-PSO. It consists of two steps: MAP estimation and PSO localization. In MAP estimation, we analyze nodes&rsquo, mobility patterns, which provide the priori knowledge for localization, and characterize distance measurements under the assumption of additive and multiplicative noises, which serve as the likelihood information for localization. Then the priori and likelihood information are fused to derive the localization objective function. In PSO localization, a swarm of particles are used to search the best location solution from local and global views simultaneously. Moreover, we eliminate the localization ambiguity using a novel reference selection mechanism and improve the convergence speed using a bound constraint mechanism. In the simulations, we evaluate the performance of the proposed algorithm under different settings and determine the optimal values for tunable parameters. The results show that our algorithm outperforms the benchmark method with high localization accuracy and low energy consumption.

Surveillance is a critical problem for harbor protection, border control or the security of commercial facilities. The effective protection of vast near-coast sea surfaces and busy harbor areas from intrusions of unauthorized marine vessels, such as pirates smugglers or, illegal fishermen is particularly challenging. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental Wireless Sensor Network (WSN) on the sea's surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect any passing ships by distinguishing the ship-generated waves from the ocean waves. We design a three-tier intrusion detection system with which we propose to exploit spatial and temporal correlations of an intrusion to increase detection reliability. We conduct evaluations with real data collected in our initial experiments, and provide quantitative analysis of the detection system, such as the successful detection ratio, detection latency, and an estimation of an intruding vessel's velocity.

Localization is one of the critical services in Underwater Acoustic Sensor Networks (UASNs). Due to harsh underwater environments, the nodes often move with currents continuously. Consequently, the acoustic signals usually propagate with varying speeds in non-straight lines and the noise levels change frequently with the motion of the nodes. These limitations pose huge challenges for localization in UASNs. In this paper, we propose a novel localization method based on a variational filtering technique, in which the spatial correlation and temporal dependency information are utilized to improve localization performance. In the method, a state evolution model is employed to characterize the mobility pattern of the nodes and capture the uncertainty of the location transition. Then, a measurement model is used to reflect the relation between the measurements and the locations considering the dynamics of the acoustic speed and range noise. After that, a variational filtering scheme is adopted to determine the nodes&rsquo, locations, which consists of two phases: variational prediction and update. In the former phase, the coarse estimation of each node&rsquo, location is computed based on its previous location, in the latter phase, the coarse location is optimized by incorporating the measurements from the reference nodes as precisely as possible. At last, an iterative localization scheme is applied, in which a node labels itself as a reference node if the confidence of its location estimation is higher than the predefined threshold. We conducted extensive simulations under different parameter settings, and the results indicate that the proposed method has better localization accuracy compared to a typical SLMP algorithm while maintaining relatively high localization coverage. Moreover, spatial&ndash, temporal variational filtering (STVF) is more robust to the change of the parameter settings compared to SLMP.

Focal cortical dysplasia (FCD) is a common cause of pharmacologically-intractable epilepsy, however, the precise mechanisms underlying the epileptogenicity of FCD remains to be determined. Neuropeptide Y (NPY), an endogenous anticonvulsant in the central nervous system, plays an important role in the regulation of neuronal excitability. Increased expression of NPY and its receptors has been identified in the hippocampus of patients with mesial temporal lobe epilepsy, presumed to act as an endogenous anticonvulsant mechanism. Therefore, we investigated whether expression changes in NPY receptors occurs in patients with FCD. We specifically investigated the expression of seizure-related NPY receptor subtypes Y1, Y2, and Y5 in patients with FCD versus autopsy controls. We found that Y1R and Y2R were up-regulated at the mRNA and protein levels in the temporal and frontal lobes in FCD lesions. By contrast, there was no significant change in either receptor detected in parietal lesions. Notably, overexpression of Y5R was consistently observed in all FCD lesions. Our results demonstrate the altered expression of Y1R, Y2R and Y5R occurs in FCD lesions within the temporal, frontal and parietal lobe. Abnormal NPY receptor subtype expression may be associated with the onset and progression of epileptic activity and may act as a therapeutic candidate for the treatment of refractory epilepsy caused by FCD.

Ocean covers nearly 71 % of our planet’s surface and deploying wireless sensor networks on the sea can perform monitoring tasks over the oceans. With some applications, we may deploy sensors both on the sea surface and underwater which forms a Hybrid Ocean Sensor Networks (HOSNs). In this paper, we discussed a localization scheme with the hybrid wireless sensor networks which combines the advantages of Wireless sensor networks (WSNs) and Underwater acoustic-sensor networks (UWA-SNs). We first introduced the structure of the hybrid wireless sensor networks and its applications, then we discussed a localization schemes MLA for moored nodes deployed underwater. We present the related work and finally discussed our future research of the hybrid ocean wireless sensor networks.

Surveillance is a vital problem for harbor protection, border control or the security of other commercial facilities. It is particularly challenging to protect the vast near-coast sea surface and busy harbor areas from intrusions of unauthorized marine vessels, such as trespassing boats and ships. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental wireless sensor network on the sea surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect the passing ships by distinguishing the ship-generated waves and the ocean waves. We design an intrusion detection system in which we propose to exploit spatial and temporal correlations of the intrusion to increase detection reliability. We conduct evaluations with real data collected by our initial experiments, and provide quantitative analysis on the detection system, such as the successful detection ratio and the estimation of the intruding ship velocity.

In this paper, we propose a novel distributed localization scheme LDB, a 3D localization scheme with directional beacons for Underwater Acoustic Sensor Networks (UWA-SNs). LDB localizes sensor nodes using an Autonomous Underwater Vehicle (AUV) as a mobile beacon sender. Mounted with a directional transceiver which creates conical shaped directional acoustic beam, the AUV patrols over the 3D deployment volume with predefined trajectory sending beacons with constant interval towards the sensor nodes. By listening two or more beacons sent from the AUV, the nodes can localize themselves silently. Through theoretical analysis, we provide the upper bound of the estimation error of the scheme. We also evaluate the scheme by simulations and the results show that our scheme can achieve a high localization accuracy, even in sparse networks.

In this paper, we present a practical wireless sensor network for environmental monitoring (OceanSense) deployed on the sea. The system is mainly composed of TelosB motes, which are deployed on the surface of the sea collecting environmental data, such as temperature, light and RSSI from the testbed. The motes communicate with a base station, which transmits collected data to a visualization system running on a database server. The data can be accessed using a browser-based web application. The OceanSense has been running for more than half a year, providing environmental monitoring data for further study.

Underwater sensor networks (UWSN) are widely used in many applications, such as oceanic resource exploration, pollution monitoring, tsunami warnings and mine reconnaissance. In UWSNs, determining the location information of each sensor node is a critical issue, because many services are based on the localization results. In this paper, we introduce a novel underwater localization approach based on directional signals, which are transmitted by an autonomous underwater vehicle (AUV). Our method utilizes directional beacons (UDB) to replace traditional omni-directional localization which provides more accurate and efficient ways to locate the sensors themselves by simple calculations. The advantage of this novel scheme is that the communications between AUV and sensors are not necessary because the AUV broadcasts signals and sensors only need to passively listen to the signals. Since the energy consumption for transmissions in underwater environments is a nontrivial factor, our localization scheme not only supports accurate positioning, but also reduces energy consumption of sensors. We evaluate our scheme by simulations. The results show that our new approach is very precise in a strap area. At the same time, we minimize the number of beacons issued from the AUV.

Wireless sensor networks have been applied to monitor pipeline structural health. In these networks, expensive multi-sinks with energy harvesting modules are deployed along the linear pipeline, and battery powered sensor nodes are deployed between the sinks. One of the main problems in such networks is the unbalance of energy consumption of sensor nodes, which makes the whole monitoring system lose its functionality with only a small percentage of sensor nodes depleted of their energy. In this paper, we propose a distributed sensing data propagation algorithm based on graded residual energy (GRE) of the sensor nodes, in order to achieve balanced energy consumption among sensor nodes. The optimum number of energy grades of GRE has been calculated through theoretical analysis in terms of maximizing network lifetime. The simulation results have shown that GRE can achieve balanced energy consumption between the sensor nodes and at the same time prolong the lifetime of the whole monitoring system.