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Recently, data from many sensors has been used in a disaster monitoring of things, such as river wa- ter levels, rainfall levels, and snowfall levels. These types of numeric data can be straightforwardly used in a further analysis. In contrast, data from CCTV cameras (i.e. images and/or videos) cannot be easily interpreted for users in an automatic way. In a tra- ditional way, it is only provided to users for a visual- ization without any meaningful interpretation. Users must rely on their own expertise and experience to interpret such visual information. Thus, this paper proposes the CNN-based method to automatically in- terpret images captured from CCTV cameras, by us- ing snow scene segmentation as a case example. The CNN models are trained to work on 3 classes: snow, non-snow and non-ground. The non-ground class is explicitly learned, in order to avoid a confusion of the models in differentiating snow pixels from non- ground pixels, e.g. sky regions. The VGG-19 with pre-trained weights is retrained using manually la- beled snow, non-snow and non-ground samples. The learned models achieve up to 85% sensitivity and 97% specificity of the snow area segmentation.

Deep Learning (DL) training requires efficient access to large collections of data, leading DL frameworks to implement individual I/O optimizations to take full advantage of storage performance. However, these optimizations are intrinsic to each framework, limiting their applicability and portability across DL solutions, while making them inefficient for scenarios where multiple applications compete for shared storage resources.We argue that storage optimizations should be decoupled from DL frameworks and moved to a dedicated storage layer. To achieve this, we propose a new Software-Defined Storage architecture for accelerating DL training performance. The data plane implements self-contained, generally applicable I/O optimizations, while the control plane dynamically adapts them to cope with workload variations and multi-tenant environments.We validate the applicability and portability of our approach by developing and integrating an early prototype with the TensorFlow and PyTorch frameworks. Results show that our I/O optimizations significantly reduce DL training time by up to 54% and 63% for TensorFlow and PyTorch baseline configurations, while providing similar performance benefits to framework-intrinsic I/O mechanisms provided by TensorFlow.

Empirical Dynamic Modeling (EDM) is a state-of-the-art non-linear time-series analysis framework. Despite its wide applicability, EDM was not scalable to large datasets due to its expensive computational cost. To overcome this obstacle, researchers have attempted and succeeded in accelerating EDM from both algorithmic and implementational aspects. In previous work, we developed a massively parallel implementation of EDM targeting HPC systems (mpEDM). However, mpEDM maintains different backends for different architectures. This design becomes a burden in the increasingly diversifying HPC systems, when porting to new hardware. In this paper, we design and develop a performance-portable implementation of EDM based on the Kokkos performance portability framework (kEDM), which runs on both CPUs and GPUs while based on a single codebase. Furthermore, we optimize individual kernels specifically for EDM computation, and use real-world datasets to demonstrate up to $5.5\times$ speedup compared to mpEDM in convergent cross mapping computation., 8 pages, 9 figures, accepted at Practice & Experience in Advanced Research Computing (PEARC'21), corresponding authors: Keichi Takahashi, Gerald M. Pao

The definitive version will be published in CHI 2021, May 8–13, 2021, Yokohama, Japan; International audience; Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and humancomputer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics.

Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework. The latest implementation of EDM, cppEDM, has only been used for small datasets due to computational cost. With the growth of data collection capabilities, there is a great need to identify causal relationships in large datasets. We present mpEDM, a parallel distributed implementation of EDM optimized for modern GPU-centric supercomputers. We improve the original algorithm to reduce redundant computation and optimize the implementation to fully utilize hardware resources such as GPUs and SIMD units. As a use case, we run mpEDM on AI Bridging Cloud Infrastructure (ABCI) using datasets of an entire animal brain sampled at single neuron resolution to identify dynamical causation patterns across the brain. mpEDM is 1,530 X faster than cppEDM and a dataset containing 101,729 neuron was analyzed in 199 seconds on 512 nodes. This is the largest EDM causal inference achieved to date., Comment: 10 pges, 10 figures, accepted at IEEE International Conference on Parallel and Distributed Systems (ICPADS)2020, corresponding authors: Keichi Takahashi, Gerald M Pao

Large-scale distributed systems have become an essential part of our everyday life. These systems have a large number of hardware and software components, often cooperating in complex and unpredictable ways. Operating these kinds of systems requires centralized monitoring to understand their overall states. While running software to collect metrics in a server is considered common nowadays, it often goes unstudied the impact metric collection software have on the base system. This is especially important in low-power, IoT applications. According to our review, one particular software, Telegraf, has never been formally studied before in terms of how much overhead Telegraf adds to the base system. In this work, we conducted several experiments to study how the base system is affected by Telegraf in two scenarios: a datacenter server and an IoT node. The results show that Telegraf is lightweight and suitable to serve as a real-time monitoring agent in both scenarios.

R2R Thailand gathers thousands of medical Routine to Research project data in Thailand and transforms into a valuable knowledge resource for improving healthcare services and supporting healthcare policymakers. MedThaiVis was developed to serve as a tool to visualize these large and complex data and extended to operate on the Scalable Adaptive Graphics Environment (SAGE2). This platform helps healthcare policymakers to use MedThaiSAGE, a Decision Support System based on Association Rules, visualizes insights of the R2R complex data on the SAGE2 high-resolution monitor wall assisting in a policy development. However, MedThaiSAGE has a limitation in terms of visualization from MedThaiVis and these two platforms perform independently. Therefore, we propose an approach to integrate these two platforms in order to enhance their capability. There are three main issues: 1) data integration, 2) communication and cooperative workflow between the two platforms, and 3) fully support on SAGE2. In conclusion, our approach can help users to explore the overview and insights of R2R data and increase the capability to support healthcare policymakers.

LandSAGE is a program meant to advocate and train scientists and policy makers in Southeast Asia countries, such as Thailand, Vietnam, Cambodia, and Laos, to use collaborative large display systems (CyberCANOEs) to monitor and mitigate landslides. In this late breaking work we provide an overview of the first phase (out of three) of this program spread over five workshops conducted in Southeast Asia. We detail a design workshop meant to understand the needs of the local scientists and adapt them to large displays, an initial prototype we developed running on the SAGE2 platform, and conclude with some of the challenges we have encountered while bringing our workshops to Southeast Asia.

A digital poster is a new presentation media, which can be used instead of the traditional paper poster for presenting information at technical conferences or exhibitions. Managing multiple different digital posters on different remote displays requires a new approach that allows flexible and updateable content control across the remote displays. This paper proposes a digital poster management system on the multiple display environments using the SAGE2 middle-ware. The proposed system consists of a single master server and multiple remote servers. The master server automatically detects the remote servers and create a controller for each remote server. To organize digital posters, the user manipulates a remote server through the corresponding controller. We demonstrate a cost-effective solution for organizing digital posters over multiple remote displays using SAGE2 and Raspberry PI computers.

Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

Recent experimental evidence points to the possibility that cell surface-associated caveolae may participate in mechanotransduction. The particular shape of caveolae suggests that these structures serve to prevent exposure of putative mechanosensors residing within these membrane invaginations to shear stresses at magnitudes associated with initiation of cell signaling. Accordingly, we numerically analyzed the fluid flow in and around caveolae using the equation of motion for flow of plasma at low Reynolds numbers and assuming no slip-condition on the membrane. The plasma velocity inside a typical caveola and the shear stress acting on its membrane are markedly reduced compared to the outside membrane. Computation of the diffusion field in the vicinity of a caveola under flow, however, revealed a rapid equilibration of agonist concentration in the fluid inside a caveola with the outside plasma. Western blots and immunocytochemistry support the role of caveolae as shear stress shelters for putative membrane-bound mechanoreceptors such as flk-1. Our results, therefore, suggest that caveolae serve to reduce the fluid shear stress acting on receptors in their interior, while allowing rapid diffusion of ligands into the interior. This mechanism may permit differential control of flow and ligand activation of flk-1 receptor in the presence of ligands.

Future Internet (FI) test beds are experimentation platforms comprising different types of resources and located in various geographical locations. The ability to leverage heterogeneous resources across these infrastructures is a key concept in the creation of future intelligent network technologies, which are important for cyberphysical systems, the Internet of Things (IoT), and many other large-scale systems. This is generally achieved with programmable networks that are based on software-defined networking (SDN) principles [1]. SDN decouples the control plane from the data plane, thereby increasing the flexibility in the underlying network control and management. Both academic and industrial SDN researchers are carrying out substantial research and development on the FI worldwide using these large-scale test-bed infrastructures [2]. These efforts focus on the integration of networks, compute, and other resources using increasingly intelligent solutions. This federation of network islands results in a software-defined infrastructure (SDI) that mimics the real-world environment for testing large-scale, innovative network applications, algorithms, protocols, or network functions. Researchers use common interfaces and workflows in a federation, which abstracts different internal infrastructures, resources, and procedures to facilitate the execution of experiments. However, a federation of open, heterogeneous test beds is nontrivial and requires an appropriately intelligent architecture and framework.

SAGE (the Scalable Adaptive Graphics Environment) and its successor SAGE2 (the Scalable Amplified Group Environment) are operating systems for managing content across wideband display environments. This paper documents the prevalent usage patterns of SAGE-enabled display walls in support of the e-Science enterprise, based on nearly 15 years of observations of the SAGE community. These patterns will help guide e-Science users and cyberinfrastructure developers on how best to leverage large tiled display walls, and the types of software services that could be provided in the future.

Images from CCTV cameras can be used for analyzing disaster situations in a particular area. Snowfall is one of the weather conditions that could cause natural disasters in Japan. It is possible for a machine to detect snow and mark these areas in that image. There are existing convolutional neural network-based (CNN-based) frameworks that can achieve high accuracy in an object classification task. However, these frameworks cannot define or mark the affected area then display the result. To address this problem, this paper proposes a method to develop a model using CNN frameworks, with the transfer learning technique. We use transfer learning to reduce training time and computing resources while maintaining high performance in a snow detection task. For transfer learning, the pre-trained weights from the VGG19 dataset is used. In this work, we use images from CCTV cameras, which were obtained from a publicly accessible website in Japan. After evaluating the model we performed post-processing on the data to further reduce the error in the results. Our proposed method can achieve an average sensitivity of 80.90% and specificity 98.54% for snow detection in real-world images.

BackgroundRice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci (QTL) discovery and molecular marker development. Comparative sequence analyses across QTL regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.FindingsWe adopted the Galaxy framework to build the federated Rice Galaxy resource, with shared datasets, tools, and analysis workflows relevant to rice research. The shared datasets include high density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from nine published rice genomes. Rice Galaxy includes tools for designing single nucleotide polymorphism (SNP) assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.ConclusionsRice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

The Scalable Amplified Group Environment (SAGE2) is an open-source, web-based middleware for driving tiled display walls. SAGE2 allows running multiple applications at once within its workspace. In large display walls, users tend to collaborate using multiple applications in the same space for simultaneous interaction and review. Unfortunately, many of these applications were created independently by different developers and were never intended to interoperate which greatly limits their potential reusability. SAGE2 developers face system limitations where applications are data segregated and cannot easily communicate with others. To counter this problem, we developed the SAGE2 data sharing components. We describe the Sage River Disaster Information (SageRDI) application and the SAGE2 architectural implementations necessary for its operation. SageRDI enables river.go.jp, an existing website that provides water sensor data for Japan, to interact with other SAGE2 applications without modifying the website's server, hosted files, nor any of the default SAGE2 applications.

Digital posters are an impressive presentation media that can replace paper posters used at technical conferences. An organizer of such events requires effective management of multiple digital posters and multiple monitors. This paper proposes a digital poster management system on a multiple display environment. To control multiple monitors, we employ SAGE2 technology, which is middleware for collaborative working in high resolution that allows multiple users to simultaneously control the display of the same monitor from browsers. Managing multiple monitors is designed using the main SAGE2 screen to manage and control other SAGE2 screens. We have implemented a cost-effective multiple display system using Raspberry Pi computers. For digital posters, we have extended a PDF viewer to support automatic page advancing with an arbitrary interval. Also, we have developed a SAGE2 multiple monitor application that can be used to control the PDF viewer presentation on multiple SAGE2 displays. The current implementation can manage and control what should be shown on each SAGE2 display. Future work includes implementing a PDF uploading function and evaluating the performance and effectiveness in a real exhibition environment.

The interest in augmented reality (AR) and virtual reality (VR) for a variety of applications has surged in recent years. Although these technologies provide new ways to interact and understand information, often the experience is limited to a single person. To address this limitation, this paper uses the concept of spatial anchors from Microsoft using Holotoolkits that to create a collaborative AR environment for HoloLens application. The collaborative environment can work through the server in Holotoolkits running with the Unity platform. Our collaborative AR environment will be demonstrated with a disaster management application that can be shared in the HoloLens as a proof-of-concept application. The concept of our application can be implemented in a variety of use-cases to provide greater collaborative understanding including an indoor navigation system that shares user’s view point in HoloLens. Ultimately, this environment will be used to visualize, explore, and understand large datasets in data-intensive science.

Virtual high-throughput biochemical screening offers a cost-effective alternative to the empirical testing of millions of compounds. However, virtual screening data often requires some manual processing of the data to eliminate false positives, evaluate the ligand-macromolecule fit, and identify new molecular interactions. This analysis is generally hindered by highly specific software and hardware requirements and complex user interfaces. Hydra is an HTML5 and JavaScript based application, which ameliorates this issue by displaying ligand-macromolecule models calculated by virtual screening programs in a single, simple online interface. The application is capable of loading raw data sets from the DOCK virtual screening platform and utilizing pre-processed datasets from other software to display compounds side-by-side in a user-defined size grid of 3Dmol.js instances. It also searches databases for selected compound information to natively display within the interface. This tool provides a highly accessible platform for streamlined virtual screening results analysis.

A number of medical research is produced by healthcare organizations in Thailand from their routine work, called Routine to Research (R2R). The information from R2R is becoming more important and can be analyzed to find hidden useful knowledge, which may lead to policy of the country. To analyze the data, Association Rule Finding technique and Knowledge Discovery are applied to find relevant patterns and relationship of the related variables in R2R data. The result can be used as evidence for policy makers in the process of policy making. In addition, MedThaiSAGE--a web-based application is also developed to allow users to see the relevant set of rules and visualize related information. The results show that there are some interesting rules that likely to be used by Public Health Policy Makers to develop a policy in order to improve and provide Thai people better health care services.

With the ever-increasing amount of information, data scientists continue to explore new technologies that will help them access and interact with data in a variety of domains and situations. One technology of particular interest is Scalable Resolution Shared Displays (SRSD) that use a web-based collaboration middleware called SAGE2. These display systems are ideal for exploring large data sets in data intensive applications; however, interacting with content on these largewalls intuitively and rapidly remains a challenge to be addressed. This work introduces a prototype user interface basedon a simple, hand gesture-based approach to control content in a SAGE2 workspace using the Leap Motion controller. Our implementation and preliminary testing of the interface demonstrates its potential as a more natural interaction modalitywhen exploring big data sets.

At the present, the security on the Internet is very sensitive and important. Most of the computer science curricula in universities and institutes of higher education provides this knowledge in term of computer and network security. Therefore, students studying in the information technology area need to have some basic knowledge about the security in order to prevent the potential attacks and protect themselves from hackers or intruders. Unfortunately, the network security concept is moderately abstract when students learn in the traditional lecture-based class. In this paper, to motivate and help students to perceive better than in the traditional classroom, we propose a security game called “Lord of Secure”, which is a virtual reality (VR) game on Android for education. It is an alternative learning materials for learners to gain the knowledge about the network security effectively. The game composes of main topics of the network security such as Firewall, IDS, IPS, and Honey pot. Moreover, the game will give the players knowledge about network security through the virtual world. The game also contains several quizzes including pretest and posttest, so players will know how much they gain more knowledge about network security by comparing scores before and after playing the game.

Network security is an important topic in the security class for computer science students. Unfortunately, the network security concept is moderately abstract and challenging when students learn in the traditional lecture-based class. The knowledge can be delivered to students better in the form of edutainment, i.e. the combination between education and entertainment. This edutainment technology will help students to learn difficult topics more enjoyably and more efficient. This paper discusses the development of a novel virtual reality (VR) application called VRFiWall, destined to educating the Firewall concept, which is one of important topics in network security class. Our edutainment game is designed for university-level students and can be alternatively used to support in lecture-based class. Moreover, our game also supports both the desktop and mobile platforms, thus students can practice the game and review the security concepts easily via their mobile phones anywhere and anytime as well.

Medical Research Project data in Thailand is currently playing an important role in Thai medical research for Thai Public Health practitioners and decision makers. In order to effectively visualize such complex data, large-scale highresolution screens are needed. Therefore, we explored the compatibility of these large-scale displays with this data through the creation of a Scalable Amplified Group Environment (SAGE2) native application that allows us to display various Thai medical research project data visualizations existing in web and cloud based application of MedThaiVis. By using the multiple high-resolution tiled screen walls driven by SAGE2 middleware, users will work and interact with large amounts of data that could not be displayed clearly on traditional desktop screen due to their size. This work serves as a foundation to support a new collaborative environment for Thai public health decision makers and practitioners in the future.

This paper aims at building a scalable MQTT broker by combining low-cost board computers like Raspberry Pi and open source broker software. The MQTT broker can be used to exchange messages between devices and devices in term of publish/subscribe for create Internet of Things network. Nowadays people extremely interest in IoT not only in small room or home but expand to the big company which have a huge amount of IoT devices. Thus, we propose a scalable and low cost MQTT broker clustering which can implement easily but have high performance with a load balancer for a single entry point of MQTT messages and balance the message in clustering. We decide to use Raspberry Pi as it have low cost and can run linux OS with Docker and have enough spec for MQTT broker. This paper also shows the performance of the Raspberry Pi cluster and how to visualize the status of a computer cluster on SAGE2 wall.

Programmable networks are a substantial part of current R&D on future internet (FI) in Europe and worldwide, with considerable impact generated by large-scale test bed infrastructures. In such test beds, researchers validate proof-of-concept prototypes for new algorithms and mechanisms for efficiently controlling and managing network resources. One of the key domains for FI research is software-defined networking (SDN), which creates innovations in existing Internet architectures by shifting the control and logic outside the network equipment to Data Centres. International cooperation among leading research centres in Europe, Americas and Asia is key to validate SDN foundations and tools. EU and Japan have jointly funded the FELIX project (federated test-beds for large-scale infrastructure experiments), which defines a common control and orchestration framework to manage federated FI test beds across continents. This framework enables an experimenter to (i) request and obtain resources across different test bed infrastructures dynamically; (ii) manage and control the network paths connecting the federated SDN test beds; (iii) monitor the underlying resources and (iv) use distributed applications executed on the federated infrastructures. This paper describes the high-level architecture of the FELIX framework and details six use cases that will be employed for validation. We present our analysis and end-user considerations, highlighting the necessity for resource accessibility and coherent use of physical connections over a large-scale test bed where different control technologies such as OpenFlow and the network service interface (NSI) are simultaneously used.

Summary The feasibility of using embedded systems in real-life applications is becoming more widespread. These applications have grown from do-it-yourself projects of computer enthusiasts or robotics projects to larger scale efforts and deployments. This paper describes a scenario that deployed a prototype application that allows the public to interact with features of a model and view videos from a first-person perspective on the train. Through testing the embedded systems and their usage in a public setting, it was demonstrated that interactive features could be implemented in model train exhibits, which are featured in traditional museum environments that lack technical infrastructure. Specifically, the Arduino and Raspberry Pi provide the necessary linkages between the Internet and hardware, allowing for a greater interactive experience for museum visitors. These results provide an important use-case scenario and lessons learned that cultural heritage institutions can use when implementing embedded systems on a larger scale, for the purpose of increasing visitors' experience through greater interaction and engagement.

The ability to migrate services to a remote data center is an important feature of Cloud services, especially after a natural disaster. The FELIX project has developed a global scale testbed by federating multiple, distributed, data centers, called SDN islands. FELIX leverages on a transit network resource manager (TN RM) to enable NSI and GRE connections between different SDN islands. In this study, we perform an IaaS migration over the EU-Japan FELIX federated testbed and NSI-enabled network. The experimental results show that IaaS migration in a low latency environment performs high-speed migration, while in a high latency environment, communication optimization achieves a reasonable 10-minute IaaS migration.

The drug discovery process in general is a very resource intensive undertaking that has existed for a very long time. In the last two decades, performing molecular simulations that determine the level of interaction between a protein and ligand have been refined to the point where they are now an essential part of the drug discovery process. These simulations serve to reduce the time to discovery and improve the positive “hit” rates when screening for molecule with biological activity. As a result, the chemical search space is greatly reduced in silico, prior to any in vitro experiments that validate the results. Recently, there have been many advances in computer science technologies that have improved the virtual screening process. This paper will give a brief overview of the virtual screening process and then summarize the current state-of-the-art technologies applied to virtual screenings. Both biomedical researchers and computer scientists can use this review as a guide to the implementation requirements for computational resources of virtual screening.

FELIX federates existing Future Internet (FI) experimental facilities across continents to build a test environment for large-scale SDN experiments. The management framework developed by FELIX allows the execution of experimental network services in a distributed environment comprised of heterogeneous resources. The demonstration described in this paper showcases the implementation of the FELIX architecture over the federated experimental facilities across Japan and Europe leveraging on both the infrastructure resources and the FELIX management stack. The presented use-case also provides an important experimental scenario for data center operators who are developing Business Continuity Planning for IT services.

This study investigated interactions between the effects of mechanical stretch and thrombin on RhoA activation in rat aortic smooth muscle cells (RASMC). Equibiaxial, pulsatile stretch, or thrombin produced a significant increase in RhoA activation. Surprisingly, in combination, 30 min of stretch inhibited the ability of thrombin to activate RhoA. NO donors and 8-bromo-cGMP significantly inhibited thrombin-induced RhoA activation. Interestingly, the nitric oxide synthase (NOS) inhibitor l -NAME increased basal RhoA activity, suggesting that NOS activity exerts a tonic inhibition on RhoA. Stretching RASMC increases nitrite production, consistent with the idea that NO contributes to the inhibitory effects of stretch. Thrombin stimulates MAP kinase and NF-κB pathways through Rho and these responses were blocked by 8-bromo-cGMP or stretch and restored by l -NAME. These data suggest that stretch, acting through NO and cGMP, can prevent the ability of thrombin to stimulate Rho signaling pathways that contribute to pathophysiological proliferative and inflammatory responses.

Sphingosine-1-phosphate (S1P) induces a transient bradycardia in mammalian hearts through activation of an inwardly rectifying K+ current ( IKACh) in the atrium that shortens action potential duration (APD) in the atrium. We have investigated probable mechanisms and receptor-subtype specificity for S1P-induced negative inotropy in isolated adult mouse ventricular myocytes. Activation of S1P receptors by S1P (100 nM) reduced cell shortening by ∼25% (vs. untreated controls) in field-stimulated myocytes. S1P1 was shown to be involved by using the S1P1-selective agonist SEW2871 on myocytes isolated from S1P3-null mice. However, in these myocytes, S1P3 can modulate a somewhat similar negative inotropy, as judged by the effects of the S1P1 antagonist VPC23019 . Since S1P1 activates Gi exclusively, whereas S1P3 activates both Gi and Gq, these results strongly implicate the involvement of mainly Gi. Additional experiments using the IKACh blocker tertiapin demonstrated that IKACh can contribute to the negative inotropy following S1P activation of S1P1 (perhaps through Giβγ subunits). Mathematical modeling of the effects of S1P on APD in the mouse ventricle suggests that shortening of APD (e.g., as induced by IKACh) can reduce L-type calcium current and thus can decrease the intracellular Ca2+ concentration ([Ca2+]i) transient. Both effects can contribute to the observed negative inotropic effects of S1P. In summary, these findings suggest that the negative inotropy observed in S1P-treated adult mouse ventricular myocytes may consist of two distinctive components: 1) one pathway that acts via Gi to reduce L-type calcium channel current, blunt calcium-induced calcium release, and decrease [Ca2+]i; and 2) a second pathway that acts via Gi to activate IKACh and reduce APD. This decrease in APD is expected to decrease Ca2+ influx and reduce [Ca2+]i and myocyte contractility.

Sphingosine-1-phosphate (S1P) is a low-molecular-weight phospholipid derivative released by activated platelets. S1P transduces signals through a family of G protein-coupled receptors to modulate various physiological behaviors of endothelial cells. Platelet/endothelial cell adhesion molecule-1 (PECAM-1; CD31) is a 130-kDa protein expressed on the surfaces of leukocytes, platelets, and endothelial cells. Upon PECAM-1 activation, its cytoplasmic tyrosine residues become phosphorylated and bind with SH2 domain-containing proteins, thus leading to the downstream functions mediated by PECAM-1. In the present study, we found that S1P induced PECAM-1 tyrosine phosphorylation and SHP-2 association in bovine aortic endothelial cells (BAECs) by immunoprecipitation and western blotting. The pretreatment of BAECs with a series of chemical inhibitors to determine the signaling pathway showed that the PECAM-1 phosphorylation was inhibited by PP2, indicating the participation of Src family kinases. These results demonstrated that S1P induced PECAM-1 tyrosine phosphorylation in BAECs through mediation of Src family kinases, and this may regulate the physiological behaviors of endothelial cells.