When one watches a sports game, one may feel her/his own muscles moving in synchrony with the player's. Such parallels between observed actions of others and one's own has been well supported in the latest progress in neuroscience, and coined “mirror system.” It is likely that due to such phenomena, we are able to learn motor skills just by observing an expert's performance. Yet it is unknown whether such indirect learning occurs only at higher cognitive levels, or also at basic sensorimotor levels where sensorimotor delay is compensated and the timing of sensory feedback is constantly calibrated.Here, we show that the subject's passive observation of an actor manipulating a computer mouse with delayed auditory feedback led to shifts in subjective simultaneity of self mouse manipulation and auditory stimulus in the observing subjects. Likewise, self adaptation to the delayed feedback modulated the simultaneity judgment of the other subjects manipulating a mouse and an auditory stimulus. Meanwhile, subjective simultaneity of a simple visual disc and the auditory stimulus (flash test) was not affected by observation of an actor nor self-adaptation.The lack of shift in the flash test for both conditions indicates that the recalibration transfer is specific to the action domain, and is not due to a general sensory adaptation. This points to the involvement of a system for the temporal monitoring of actions, one that processes both one's own actions and those of others. The ability to change one's behavior according to the observation of another person's experience has no doubt contributed to adaptive behavior in the natural and social environments throughout the evolution of mankind. It is obvious that we learn from the success and failure of others at the cognitive level, where we follow or avoid the observed action/strategy when put in similar situations. Also at the level of motor skills, imitation learning is an important aspect of our motor behavior and has been studied extensively The precise temporal order/timing of self-motor action and external events are crucial as it signals the causal relationship of the two events A more classical example of sensorimotor recalibration can be found in the spatial domain with prism adaptation. Prism adaptation is a phenomenon in which the motor system adapts to new visuospatial coordinates imposed by prisms that displace the visual field. Helmoholtz first described adaptation and after-effect on reaching movements in subjects wearing prism glasses self adaptation (motor-auditory adaptation) leads to change in simultaneity judgments of other's motion and sound . We have switched the modality between self/other adaptation and other/self test to avoid interference from multisensory adaptation effects. If one's limb was visible during self adaptation and self test, viewpoint invariant (one's own arm and other's arm) “multi-sensory” adaptation between visual limb motion and auditory feedback would mix in to the effect of transfer of “sensorimotor” adaptation between the self and other.Though, in many cases where sensory and motor relationships are subject to recalibration, either spatial or temporal, it has been assumed that adaptation occurs only when perturbation in sensory feedback is introduced in one's own action but see . In thisself adaptation and other adaptation experiment, which shared the general experimental design but differed in who experienced sensorimotor temporal adaptation, self or the other  or performing a motor action (self adaptation experiment). Previous studies have used a similar method to gradually increase the temporal delay resulting in robust adaptation In the adaptation phase, subjects were exposed to a gradually increasing temporal delay in sensory feedback starting from 100 ms to the maximum value of 235 ms while passively observing . It is important to note that the adapted pair of modalities is different between the two adaptation conditions: sensor-sensory (auditory and vision) for the other adaptation and sensory-motor (auditory and motor) for the self adaptation experiment.The motor action consisted of a single back-and-forth movement of a computer mouse, and sensory feedback was given at various delays by an auditory beep delivered at the termination of the mouse movement . In the self adaptation experiment) or to the experimenter (in case of the other adaptation experiment) so as to align the auditory beep delivered at the termination of the mouse movement. In both cases, due to the gradual slow increase of temporal delay, temporal adaptation of motor initiation compensated for the introduced artificial delay so that the metronome and the auditory feedback beep was physically aligned for majority of the time during the adaptation phase. Hence, subjective perceptual temporal recalibration, if any, would occur between auditory-beep/metronome-click and mouse movement termination .In both adaptation experiments, a metronome clicking at 1 Hz were used to provide a cognitive temporal target to the subject  at three levels of adaptation: pre-, peri- and post-adaptation. During the other test, the subject observed an experimenter manipulate a computer mouse with sensory feedback . The motor action was identical to that of the adaptation phase, while the timing of the auditory beep was experimentally manipulated to arrive randomly before and after the termination of the mouse movement. Subjects were required to make two alternative forced-choice subjective simultaneity judgments between the termination of the mouse movement and the auditory beep. Meanwhile, self test was identical to the other test except that the subjects themselves manipulated the mouse, with their arms and shoulders hidden from view. Finally, during the flash test, the subjects made simultaneity judgments between a short duration visual disc (16.7 ms) displayed on a computer screen and the auditory beep . This is a control condition meant to isolate the effect of audio-visual sensory adaptation. For example, in the other adaptation experiment, if transfer of adaptation to both self test and flash test is observed, it would indicate that both the sensorimotor and the audiovisual system were recalibrated. On the other hand, if transfer of adaptation is limited to the self test, it would indicate that audiovisual adaptation between other's motion and auditory feedback led only to sensorimotor adaptation.In the test phase, simultaneity judgments were made in three conditions, i.e. self and other adaptation) while the horizontal rows denote the type of test . The three colored lines in each figure represent the baseline-corrected subject averaged probability of simultaneity judgments obtained from the three test phases, pre-adaptation, peri-adaptation and post-adaptation . First, we found a basic adaptation effect, where the lag of peak of simultaneity judgment in the test phase was shifted for the adapted condition .  testing . No signautomatic mouse adaptation and self adaptation with automatic mouse test. Similar to the movement of the human manipulated mouse in the above experiments, a photograph of a computer mouse moved to the left, then to the right and stopped. Auditory beeps were delivered around the time of mouse stoppage for adaptation and test.Next, we conducted two additional control experiments to test whether the transfer of adaptation between self and other is specific to the action domain. An alternative interpretation of the above results could be object category specific adaptation, say, object specific properties of the computer mouse-auditory feedback were learnt regardless of the actor. In order to test this possibility, we introduced a visual stimulus of an automatically moving mouse and conducted two types of experiments, automatic mouse adaptation experiment, subjects underwent an adaptation procedure basically identical to the other adaptation experiment, except that, instead of the experimenter manipulating the mouse, the above mentioned mouse animation was used . Subjects were exposed to a gradually increasing temporal delay in auditory feedback starting from 100 ms to the maximum value of 235 ms. Two test phases were inserted after delay adaption to 100 ms and 235 ms. A test phase consisted of an automatic mouse test and a self test. During the automatic mouse test, auditory beep was played at a random timing around the time of mouse stoppage and the subjects were required to make a simultaneity judgment of the two events. Meanwhile, the self test was identical to the self/other adaptation experiments.In the self adaptation with automatic mouse test experiment, subjects were adapted as in the former self adaptation experiment. A test phase with two types of tests, automatic mouse test and self test were inserted after delay adaptation to 100 ms and 235 ms. The results are summarized in automatic mouse test for automatic mouse adaptation, self test for self adaptation), whereas the transfer of adaptation was not observed in both experiments.In the The main finding of this study was that sensory motor adaptation transferred inter-personally. Passive observation of other's actions led to a sensorimotor recalibration between the perceived timing of one's own actions and sensory feedback. Likewise, active performance led to recalibration between perceived timing of other's actions and its sensory feedback. This is especially notable given that in the self conditions, the subject could feel their own movements but could not see them; in the other conditions they could see but not feel the movements. Thus the effect shows transfer across sensory modalities flash test for both experiments indicates that the recalibration is specific to the action domain, and is not due to generalization of crossmodal sensory adaptation. This view is also supported by the additional control experiment, where we found no transfer of adaptation between passive viewing of an automatically moving mouse and self mouse manipulation.The lack of shift in the So what is the possible level of recalibration and its possible inputs? Involvement of perceptual learning, as opposed to pure instrumental learning, has been indicated by multiple studies flash test. Interestingly, robust transfer effects have been reported in a multi-sensory temporal recalibration experiment other adaptation and the flash test has the same configuration, but did not show any transfer effects. This discrepancy can be explained in the following manner. In the case of multi-sensory calibration, since it does not have a motor factor, calibration occurs between two sensory modalities. On the other hand, in sensor-motor delay adaptation, it is evident from cross-modal transfer effects other adaptation phase, there were three possible mechanisms of recalibration 1) audio-visual 2) sensorimotor (motor information from the mirror system) and 3) calibration of both systems. The fact that there was no transfer to the flash test and the automatic mouse test but to the self test indicates that 2) is the only plausible explanation. Meanwhile in the automatic mouse adaptation experiment, adaptation did not transfer to the self test, suggesting that 1) was the case. Idealistically, the only difference between other adaptation and automatic mouse adaptation is whether or not there is a human actor manipulating the mouse, providing additional information to the observer in the former case such as intention and kinematics/dynamics of the human movement. Transfer of adaptation to the self test in case of the other adaptation, but not in automatic mouse adaptation, would provide strong evidence that the mirror system was involved during adaptation. Although, we must admit that our automatic mouse adaptation was not perfect for testing the other alternative explanation, “object category specific adaptation”. We have used a movie of a mouse on the computer screen, instead of a physical mouse actually moving on its own. This lack of “realism” may have played a role in the absence of transfer to the self test. Future dedicated studies are needed to confirm the existence or non-existence of yet another interesting recalibration mechanism, “object category specific adaptation”. To summarize, although there are concerns on the validity of the automatic mouse adaptation experiment, our set of results suggest that the multi-sensory calibration system and the sensorimotor calibration system are two independent systems, and in our experimental conditions, only one system underwent recalibration for a given adaptation condition.In support of the above view, one interesting aspect of our experimental results is the lack of transfer to the One interesting point we may note is the counter-intuitive relationship between the perceptual learning aspect of delay adaptation and the observed self-other transfer. Under the assumption that the main role of supra-modal recalibration is to correct for perceptional error arising from limb growth and other bodily changes, it is quite strange that observational information of others was used as a source of recalibration. “Sensorimotor recalibration by observation” fits more nicely to the concept of “world learning”, which involves acquiring new information about the environment and adapting to it et al. have reported that temporal attraction effects can also be found in the actual timing of movements, not only in the perceived timing In relation to our experimental design, it has been shown that intention plays an important role in the temporal perception of action and sensory feedback. Haggard, Clark and Kalogeras have shown that subjects perceived voluntary movements as occurring later and its sensory feedback as occurring earlier than the physical timing, resulting in shortened perceived difference in timing, while involuntary movements induced by magnetic brain stimulation had reversed effects Finally, the reported psychophysical effect can be applied to primates and other laboratory animals to investigate an interesting twist regarding the mirror system. The human mirror system is said to have two levels of functions. The first level is that the mirror system serves as the basis of action understanding Ethics approval was obtained from the Institutional Review Board of the University of Tokyo. All of the participants gave written informed consent for their participation. A total of 18 participants (aged between 20 and 26) were used in the experiments; nine participants  in the other-adaptation experiment and nine participants  in the self-adaptation experiment.flash test and fixation spot on self test and self-adaptation appeared on a 21″ CRT monitor with a resolution of 1024 by 768 pixels and a refresh rate of 60 Hz. The viewing distance was 57 cm. A chin rest was used to maintain the participants' head position. Participants used a computer mouse for motor action and a keyboard to make responses.The stimuli were presented using the Psychophysics Toolbox self or other, was determined by the type of experiment. Half of the 18 subjects participated in the other-adaptation experiment and passively viewed an experimenter adapt to gradually increasing temporal delay in sensorimotor feedback. The experimenter and subject sat face to face, seated across a platform (48 cm in height) on which the computer mouse was placed. The motor action consisted of a single back-and-forth movement of a computer mouse and an artificially delayed sensory feedback that was given by an auditory beep delivered after the termination of the mouse movement. The experimenter performed the action repeatedly so as to produce beeps in synchrony with the clicks of a metronome at 1 Hz. The primary-adaptation phase lasted for 10 minutes where the sensorimotor delay was fixed at 100 ms. In the secondary-adaptation phase, the amount of delayed was linearly increased , and each of the delay conditions lasted 4 minutes.The two types of adaptation phases are aimed at recalibrating the subject's sensorimotor timing percept. The type of adaptation, self-adaptation experiment, and during the adaptation phase, performed the aforementioned motor action themselves. Here the platform was placed under a table (74 cm in height) and the subject took a position assisted with a chin rest and his/her shoulders and arms covered  in various conditions. First we describe the setup for the three types of test and then explain how they were conducted.Three types of tests, other test, subjects viewed an experimenter manipulating a computer mouse with auditory sensorimotor feedback  on which the computer mouse was placed. The motor action was similar to the adaptation phase, consisting of a single back-and-forth movement of a computer mouse, but with intervals of a few seconds between each trial of motion. The auditory beep was delivered before and after the termination of the mouse movement at uniformly random times that spanned ±500 ms. The subject was asked to make a two alternative forced choice (2AFC) on the subjective simultaneity of the termination of mouse movement and auditory beep. During the self test, as in the setup of self-adaptation, the platform was placed under a table (74 cm in height) and the subject took a position assisted with a chin rest and with his/her shoulders and arms out of view from the subject , 16.7 ms in duration. The subjects judged the timing of the visual disc and the auditory beep. Metronome was not used during the test phase.In the self adaptation in the self adaptation experiment and other adaptation in the other adaptation experiment. The temporal delay was fixed at 100 ms for the peri-adaptation test phase and 235 ms for the post-adaptation test phase. The above mentioned blocked procedure of top-up adaptation was used, instead of inserting top-up adaptation between every trial, because the subjects needed to move between setups in particular combinations of adaptation and test . Similar blocked top-up adaptation design with a shorter exchange interval was used in a previous study reporting positive effects of crossmodal sensor-sensory temporal recalibration self and other test, the actual timing between mouse action termination and auditory stimulus was obtained offline, and subject's responses were binned for further analysis based on this value . The timing of mouse termination was predicted by the timing of mouse direction reversal and this value was used as reference to decide the randomized onset of the auditory feedback.Test trials were coordinated into blocks of 60 trials, where the type of test was fixed within. The type order of test block was randomized and 5 blocks  were conducted for each test type during a single test phase. During the peri-adaptation and post-adaptation test phase, top-up adaptation was inserted to maintain the level of adaptation. Three minute worth of adaptation was inserted between every block, where subjects were exposed to the previously described procedure of adaptation, In order to correct for individual differences in baseline subjective simultaneity judgments, we first estimated the point of subjective simultaneity (PoSS) for the pre-adaptation test phase by fitting a Gaussian function to individual data (probability of ‘simultaneous’ response as a function of the timing difference) with a maximum-likelihood curve fitting method. The estimated pre-adaptation PoSS values from individual subjects and test conditions were independently subtracted from all timing values. Next the timing values were binned together to calculate the average subject response.A total of 18 participants (aged between 22 and 29) were used in the experiments; nine participants  in the automatic mouse adaptation experiment and nine participants  in the self adaptation (automatic mouse test) experiment.self adaptation and other adaptation experiment.The stimuli were presented using the Psychophysics Toolbox self adaptation with automatic mouse test experiment where the adaptation procedure was identical to the previously mentioned self adaptation experiment. Other half participated in the automatic mouse adaptation experiment and passively viewed an automatically moving mouse animated on a computer screen. A photograph of a mouse moved to the left (250 ms) and switched its direction to the right (250 ms) and then stopped, which mimicked the mouse motion during adaptation and test in the main experiment. An auditory beep was delivered at a timing relative to the stoppage of mouse motion depending on the stage of adaptation. The next sequence of animation started 500 ms after the stoppage of mouse motion in the previous sequence. The primary-adaptation phase lasted for 10 minutes where the delay was fixed at 100 ms. In the secondary-adaptation phase, the amount of delayed was linearly increased , and each of the delay conditions lasted 4 minutes.Half of the 18 subjects participated in the self/other adaptation experiment.Two types of tests, self test and automatic-mouse-test, were conducted after adaptation to 100 ms and 235 ms delay. Self test was identical to the one used in the automatic mouse test, subjects viewed an automatically moving mouse animated on a computer screen as in automatic mouse adaptation. The auditory beep was delivered before and after the termination of the mouse movement at uniformly random times that spanned ±500 ms. The subject was asked to make a two alternative forced choice (2AFC) on the subjective simultaneity of the termination of mouse movement and auditory beep.In the self and automatic-mouse test. Within a single test phase, the order of test was randomized and a total of 300 trials (5 blocks) were conducted for each test type. The method of constant stimuli was used to quantify two types of subjective simultaneity. For the self test, the actual timing between mouse action termination and auditory stimulus was obtained offline, and subject's responses were binned for further analysis based on this value .Multiple blocks of 60 trials were performed under the


Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper. The sensors can be deployed in ad hoc fashion in the area of interest, with no requirement for a communication infrastructure. These nodes, which include tiny processors, memory, low-power wireless radios, and physical sensors, can self-organize to form a wireless network. The low power transmissions of the sensor nodes imply that a single node's transmission has a limited range in the order of tens of meters. To overcome the limited range issue, nodes closer to the central collection node can relay the sensor information of nodes that are further away through multiple hops, until the information reaches a base station that is connected to the Internet. The base station serves as a gateway node for streaming sensor data to the users.Researchers predict that tiny sensor nodes will be deployed all over our physical world for terrestrial and marine monitoring. However, simply covering large areas with tiny sensors using is not feasible given the extremely limited communication range of these sensors.We illustrate the challenges of large scale sensor networks through the example of precision agriculture , where sA second challenge arises from static time-driven monitoring each commodity individually, which provides users with highly detailed information, but it often represents an overkill for their application needs. Synthesizing measurement information can improve the user's interpretation of the data and thus the efficiency of the decision-making process. For example, providing temperature information from each sensor within the same field is highly redundant. This causes a large energy overhead that speeds up battery depletion at the sensor nodes, and increases the cost of frequently replacing sensor batteries. Although energy efficient techniques, such as data aggregation, exist, their configuration currently requires technical know-how that is not accessible to the system end-user.Finally, using the service provider network for cluster-to-cluster and cluster-to-user communication also limits the end-user's ownership of the information paths, which may represent a security risk, as well as a cause of added cost. In current architectures, any cooperative exchanges between clusters must traverse the service provider network, which could allow a competitor or a malicious third party to intercept this information.To address these challenges, our project on Scalable and Unified Management And Control of geographically dispersed sensors (SUMAC) aims at enabling unified monitoring multiple dispersed physical areas, through an architecture which includes a medium range wireless mesh network that serves as a bridge between geographically-spread sensor node clusters and the Internet, as shown in This paper provides an overview of the SUMAC architecture and its main components, including the sensors plane, the mesh plane, and the server plane. The paper presents the sensor-related optimizations of SUMAC, including: (1) a unicast reverse routing (downstream) strategy, which builds on a distributed and unique addressing strategy, for avoiding broadcast dissemination, (2) a versatile user-configurable cost function that includes energy, delay, and reliability metrics, and (3) an adaptive fidelity feature, which enables network users to set the data resolution level based on simple high level performance policies. While the mesh network serves as a communication bridge between the sensors and the server, the SUMAC server architecture supports historical data collection from large scale deployments, real time data access, as well as high flexibility in configuring the sensors. The paper also provides empirical results from our lab testbed that explore the energy/delay tradeoff of the adaptive fidelity mechanism, and how this tradeoff can translate to autonomous delivery of user performance requirements.SUMAC provides end-to-end scalable methods that run across the three network planes for user configuration of the sensor node performance settings. These methods enable the setting of high level policies and performance targets that dynamically adapt the fidelity (resolution) of the incoming sensor data stream and the routing strategy of packets within the sensor network to events of interest in the deployment region. The high level settings by the user trigger a background process that sets the default data aggregation level (data resolution) and that determines conditions and rules for overriding or modifying the default aggregation level autonomously by the network. For instance, the default aggregation level could be set to average most of the sensor data in a single cluster, which saves energy. When an event of interest occurs, the sensors can automatically slide down the aggregation level to enable nodes to send their raw packet data in order to monitor this event's evolution more closely with higher data resolution. The background process also sets the routing cost function weights in the sensor clusters, which determines routing paths in the network and can be tailored to favour system lifetime, data quality, or high responsiveness. This process also supports a feedback mechanism, by which a change in network state resulting from a user configuration change is continuously reassessed at the server, and appropriate reconfiguration adjustments are done accordingly and autonomously by the server. While the user could set up custom alerts for getting notification of events of interest, all reconfiguration of network metrics occurs transparently according to the high level policies set by the user.The remainder of the paper is structured as follows. Section 2. positions the paper with respect to previous work on large scale sensor architectures, emerging commercial solutions, and aggregation techniques. Section 3. describes the SUMAC architecture in detail, introduces its components, and discusses each component's design features. Section 4. presents our empirical results for adaptive fidelity sensing, while section 5. discusses the results, future work, and concludes the paper.2.Many research groups and commercial companies are considering the interoperability between wireless sensor networks and other network technologies as the viable solution for the large-scale sensing coverage. The idea of using a hierarchical architecture to achieve this purpose is not novel in and of itself, but to the best of our knowledge, a comprehensive large scale monitoring solution that is accessible to non-specialized users has yet to be provided. Intel's project on “Heterogeneous Sensor Networks”  adopts aAnother interesting work is the one proposed in the ART-WiSe networks  that prowww.meshnetics.com), Arch Rock Premier Pack (www.archrock.com) and Digi Connect Port X (www.digi.com) enable communication between different networks, but simply integrating multiple wireless interfaces into the same box can cause congestion, delay and loss of important data in certain regions of the network, because they do not support transparency of state information between networks. Our work aims to develop software mechanisms that leverage existing multi-modal hardware and that provide quicker and more reliable network bridging, avoiding the creation of congestion hot spots in the network. In particular, our user-configurable cost function supports sharing of state information between the WiFi network and the sensor clusters (which use IEEE 802.15.4 technology), to enable the sensor nodes to choose the best WiFi gateway to use, based on the congestion level at potential gateways and on delay, battery level, link quality, and hop count metrics of the forwarding path from the node to the backhaul. As a result, nodes may sometimes choose a WiFi gateway that is not necessarily the nearest physical gateway in order to avoid creating congestion hotspots in the WiFi network, or to spare low-battery sensor nodes, increasing their lifetime.Several industry-based solutions have also started addressing the need for large scale sensing through heterogeneous networks as well. Existing solutions that promote integrated large scale network architectures include both hardware platforms and software packages. Hardware platforms, such as Meshnetics ZigBit Ethernet Gateway (www.archrock.com) provides one such solution that relies on Internet Protocol addressing, in an effort to bring IP technology to every sensor device, so that any commodity is addressable through the Internet. This work, referred to as 6LoWPAN [Other existing solutions for integrated large scale network architectures provide software packages for streamlining data delivery in the network. ArchRock  [Other software solutions for sensor networks are openly available. The current state-of-the-art in sensor networks provides extensive open-source network management mechanisms and protocols at the cluster level, using operating systems such as TinyOS  and Contrk (GSN)  providesOne particular optimization technique for large sensor networks is in-network aggregation, which is crucial for WSN's where energy resources are limited. Widmer et al.  present Directed diffusion  is a simA similar approach is Greedy aggregation . Greedy Our aggregation approach also considers energy costs for path generation, albeit in a slightly modified way. Nodes share their energy consumption and buffer size values with immediate neighbors. This local neighborhood information, in addition to path Etx and delay, drive routing decisions. Furthermore, our aggregation mechanism is coupled with a powerful yet simple user interface for visually setting aggregation rules on the basis of several conditions: hop count, resolution, sensor values, sensor tags, GPS coordinates.3.3.1.Geographically disjoint 802.15.4  sensor cAn 802.11  WiFi mesA remote central server that acts as a backend for a web portal that delivers added-value visualiza tion, control, storage, and analysis features to the user.The SUMAC architecture introduces an intermediate network plane, in the form of a wireless mesh network of IEEE 802.11 (WiFi) nodes, which serves as a bridge between geographically spread sensor clusters and the service provider network, as illustrated in The integration of the above network planes reduces the number of required Internet subscriptions for a given set of clusters, as the mesh network can relay sensor cluster information to the nearest Internet-connected node. The one-time deployment cost of WiFi mesh nodes is outweighed by the cost reduction of using fewer Internet subscriptions. The WiFi mesh overlay also creates an opportunity for reducing security risks and latency for communication between neighboring sensor clusters. The SUMAC architecture consolidates these three planes for seamless coordination and information delivery to users.The remainder of this section discusses each plane, including its design, challenges, and current development stage, separately.3.2.The sensors plane in SUMAC is responsible for collecting data from physical spaces and relaying this data towards a central base station in a multi-hop fashion. All nodes that report to the same base station constitute a sensor cluster. Each cluster of sensors monitors a contained physical space, such as a building or an agricultural field. The SUMAC architecture supports networks that are deployed in ad hoc fashion or on the basis of predetermined locations.A central objective of the SUMAC project is to capitalize on existing developments for intra-cluster sensor communication and to extend them for supporting the management of multiple sensor clusters through a unified interface. Achieving this objective demands addressing several challenges, which we discuss below.Challenge: The first challenge is the transparency of routing state information between the overlay WiFi network and the sensor clusters, which can lead to network congestion. For example, sensor nodes may forward data to a particular WiFi gateway if the gateway seems underused from the clusters perspective. However, the WiFi network may be highly congested at this particular gateway, but current techniques hide this information from the sensor nodes. This problem is further exacerbated in ad hoc deployments where the topologies of the sensor clusters and the mesh network are neither symmetrical nor known before deployment.Solution: To address the issues of transparency between networks and of tunable performance settings, SUMAC proposes a collaborative topology management (CTM) strategy that supports scalable, flexible, and seamless two-way access to the sensors. CTM relies on transmission of periodic beacon messages that nodes use to notify their direct neighbors about their current conditions. The CTM strat egy include an additive cost function in the beacons to compute the routing cost for each neighboring node, as follows:ETX is the Expected number of Transmissions metric, E is the energy consumption at the send ing node N, D is the delay measured by the buffer length at node N, and α, β, and γ are the user-configurable cost function weights. ETX is a standard routing metric in sensor networks that combines information on link quality and the number of hops from the base station. The energy and delay metrics have been added in the periodic beacon messages structure as well as in the routing table. Including these two metrics in the weighted cost function enables the use of high level policies to change the low-level logic that selects a routing parent considering these new parameters. Once a node receives and stores its neighbors' beacons, a dedicated timer at each node fires periodically and launches a task that selects the lowest cost neighbor in the neighbor table.α,β and γ through the graphical triangle tool in the web portal, which is discussed further in section 3.4.. Additionally, the user can adjust higher level policies to set up relative priority of metrics, which the server translates into cost function weight assignments that are disseminated to the node, for e.g. energy consumption versus lower delay and high throughput.Users can set the cost function weights, The cost function also provides visibility of network state information across gateways, so that a sensor node can dynamically determine the optimal gateway to use based on a more comprehensive set of network performance metrics. This CTM scheme exploits the concept of hierarchical beacon propagation from the WiFi mesh network to the sensor nodes. A sensor node has access to performance metrics regarding the end-to-end path: from its direct sensor parent to the backhaul connection. Our initial tests reveal this CTM strategy will significantly improve network throughput by using less congested paths to the service provider network, and it will reduce queuing delay at the gateways through the selection of the least congested gateway.During long deployments, specific nodes may detect events of interest to the network operator. The operator can set high level policies at the beginning of the deployment so that the network can autonomously react to such events without any human intervention. Alternatively, operators can choose to receive alerts for events and to manually reconfigure the affected nodes on a case-by-case basis. Both automatic and manual reconfiguration of specific sensors demand a mechanism that enables the backend server to communicate with one or few sensors selectively, to avoid the energy and communication overhead of flooding the mesh and sensor networks with reconfiguration requests that are meant for only a few sensor nodes.Challenges: Targeted sensor reconfiguration involves two challenges: (1) Unique addressing; and (2) Downstream (reverse) routing.The separate addressing strategies at each network plane in the the integrated network architecture represent a challenge to surgical node reconfiguration. While currently proposed architectures use Internet Protocol (IP) addresses to abstract out the differences in heterogeneous networks [networks , IP addrSolutions: To tackle the addressing issue, SUMAC uses a distributed and unique addressing strategy on the basis of the last 16 bits of each node's IEEE 802.x MAC layer address, rather than conventional network layer techniques. The use of MAC addresses avoids the overhead and complexity of centrally assigning and managing network layer addresses for all clusters, and it ensures that each node within the network is uniquely addressable. As a consequence, network flooding will be avoided when an end-user wants to configure a specific sensor node. The use of 802.x addressing for both the WiFi plane and the sensor plane will reduce address translation delays at gateway nodes, and it favours integration between sensor networks and mesh networks, easing the abovementioned CTM mechanism. Because some sensor node hide their MAC addresses from the operating system, we have also devised a fall-back semi-centralized mechanism for unique and dynamic address assignment.First, we can ensure that the base station node has an accessible MAC address. The base station, upon starting up, provides its MAC address to the back-end server through the mesh network. The back-end server, which has a comprehensive view of the network membership, checks whether the last 16 bits of the new base station's address conflict with any other node in the network. If so, then the server assigns a unique 16-bit address to the new base station. Upon receiving the new address, the base station then becomes a distributed address server for all nodes in its cluster, and uses the new server-assigned or server-approved address as the base address (BASE) for all addresses within its cluster. When the first node enters the cluster, the base station assigns the node the address BASE+1. The next node to join the cluster gets address BASE+2, and so on. All base stations also store the next address in their non-volatile flash memory, to maintain the state information even in the eventuality of any node resets or crashes. The address assignment strategy at the server considers other existing addresses in the deployment and avoids assigning a new address space that is numerically close to or conflicting with other existing address spaces.In case even the base station hides its MAC address from the API, the base station requests a BASE address from the Mesh Server. The Mesh Server can then assign a BASE address to this new base station. The address assignment process then proceeds within the cluster as described above.unicast. Otherwise, the node discards the packet. This reverse routing strategy aims at reducing control overhead of broadcasting requests all over the network. For the user, reverse routing enables surgical reconfiguration of specific sensor nodes with minimal overhead to other nodes.Leveraging this addressing strategy is a unicast reverse routing mechanism that enable unicast transmission of node reconfiguration requests in the downstream direction, i.e. from the base station to the sensors. Conventionally, reconfiguration requests in WSN's are broadcast to all the nodes. Even if the user requires reconfiguration of a single node, the broadcast dissemination strategies flood the network with the request. All nodes receive the packet, check if the request targets them, then rebroadcast the packet. This obviously incurs large transmission and energy overhead. Instead, sensor nodes in the SUMAC architecture snoop on all upstream messages they forward in order to extract the originating node ID of the packet and the last forwarder that sent the packet here. This snooping mechanism enable each node to construct a reverse routing table of all the nodes below it in the logical topology with no communication overhead. Having the reverse routing table available locally, when a node receives a downstream request, it checks the destination ID of the request. If the destination is available in its reverse routing table, the node forwards the packet in Challenge: The final challenge is the sheer traffic flow to and from the sensors, which can cause channel saturation at some nodes and quick depletion of battery energy. As the network includes more sensor nodes, the traffic requirements for accessing and controlling each node individually grows exponentially. This challenge is due to the inflexibility of a static reporting policy, where nodes can only report data or respond to user requests individually.Solution: To provide a more flexible and efficient means to communicate with the sensors, the SUMAC architecture enables the system user to adapt sensing fidelity from the Web portal on the basis of any combination of the following metrics:Hop count: users set an aggregation level based on a certain hop count H. Packets originating at nodes that at least H hops away from the nearest base station are aggregated.Data resolution: users set a data resolution level DR indicating how many sensor values should be aggregated. Each node in the network then checks whether it has at least DR descendants in the routing tree, and if so, the node does not aggregate its packets. Otherwise, the node aggregates its packet with all other packets awaiting forwarding in its buffer.Sensed values: users set minimum and maximum thresholds for each sensed value. If the sensed value exceeds this range at some node, the node sets a flag in its packets to override the active aggregation policy. Furthermore, users can set policies that specify whether only the node that detects an exceeded threshold, the set of neighboring nodes around it, or all the nodes in the cluster should override aggregation to deal with this event.Sensor labels: users can set tags or labels to each sensor node. For example, users can assign a label of A to a set of nodes in a vineyard and a label B to another set of nodes in a nearby forest. Users can then configure nodes with label A to aggregate their packet at a hop count of 5 or more, while setting a data resolution of 10 data points per aggregated value for label B.GPS coordinates: users set aggregation policies on the basis of geographic coordinates of the sensor nodes, by selecting nodes through the Google Maps interface of SUMAC, which is discussed further in Section 3.4.The idea here is to dynamically slide the aggregation level when nodes detect events of interest, so that the user can have more detailed information about these events, and to return to the original level when measurements return to normal values, which promotes energy efficiency, as our empirical results in Section 4. confirm. These results indicate that the energy savings of adaptive fidelity access and control of the sensors can double the operative lifetime for these battery-operated devices even in small clusters of 20 nodes. Yet another major advantage of the adaptive fidelity resides in the automation of monitoring tasks with reduced user administration; a user may set-up high level alerts and policies, so the network is configured to dynamically re-adjust the resolution of the measurements based on specific events.A final feature of the sensors plane is the feedback mechanism that constantly monitors the state of the network at the server and sends reconfiguration requests to the sensor nodes to maintain adherence to the user-set policy. Note that both relative and absolute performance settings can adapt to dynamic topology changes, including ad hoc deployment of nodes. For relative performance settings, the server remains agnostic to the network topology, as it only needs to set relative metrics. For absolute performance settings, the server considers the current topology of the network, which determines delay and energy consumption for the nodes, by examining the database. Based on the current network topology, the background process at the server determines the new network configuration and informs the sensor nodes.Sensor nodes distinguish between two types of aggregation rules: static, and dynamic. With static aggregation, the user selects nodes based on GPS coordinates, label, or ID, and sets the selected node to aggregate or to forward their data. In this case, the node aggregation rule is statically set from the server, and the node need only follow this rule until a new server request arrives for this node.With dynamic aggregation, the user sets thresholds (maximum and minimum) for the sensed values, which in the current implementation are temperature, light, and humidity. If the temperature, humidity, or light value exceeds a user-set threshold at a particular sensor, the sensor node then autonomously decides to stop aggregating the packets and it instructs neighboring nodes to do so as well, in order to monitor the region around this event of interest. This represents region-based aggregation, which users can choose to enable or disabled. If disabled, then only the sensor node that detects the event will override aggregation, while neighbors follow their normal aggregation policy.L1, and then determines the corresponding cost function weights α, β, and γ. Otherwise, if the user had initially set relative performance targets (ABS=0), the server determines new values for α, β, and γ and then a new aggregation level L1. The rules for setting the aggregation level based on the cost function weights or vice versa are determined on the basis of empirical experiments that provide rules of thumb for the delay/energy tradeoffs involved. Note that the new aggregation level L1 instructs the nodes of the new number of hops from the base station beyond which all data packets are aggregated. If L > L1, setting the new aggregation level as L1 effectively means sliding the aggregation level up, or reducing the data fidelity. In contrast, if L < L1, then setting the new aggregation level as L1 effectively means sliding the aggregation level down, or increasing the data fidelity. This is what is meant by adapting the aggregation level, or adaptive fidelity.The server then notices that some sensors have detected an event of interest, and autonomously reeval-uates the settings for this region in the network. If the user had set absolute performance targets (ABS=1), the server first determines a new aggregation level L1 and α, β, and γ the server then issues a reconfiguration request to the selected sensor nodes with these new parameters. The sensor nodes receive the reconfiguration request and adapt their settings accordingly. When the sensor nodes in the region of the event of interest no longer detect any sensor values that exceed the threshold, they return to their default aggregation/forwarding rule, as initially set by the user. This return to the default configuration is done automatically, as the sensors check whether their sensed value exceeds the threshold periodically, in particular, every time they sample the sensor values. At the first instant that a node detects that the sensed values have returned to normal, it reverts to its original configuration almost immediately.Once it determines 3.3.The mesh network plane serves as a medium range overlay that extends the coverage region of the sensor clusters and enables data convergence from multiple clusters towards a single back-end server. Gateway mesh nodes, which interface the mesh and sensors plane, can be deployed uniformly or randomly, depending on application requirements. For instance, outdoor monitoring applications provide limited, if any, access to outlet power supply for mesh nodes, which in turn constrains the placement of the gateway nodes.www.mikrotik.com), which incorporate a PowerPC architecture and supports up to 3 802.11 a/b/g interfaces. SUMAC relies on gateway nodes that integrate a sensor mote  with a mote Sky  that proWhen a mesh box is connected to a TMoteConnect device, we refer to it as a Clusterhead, which means than a java daemon is launched, called ClusterheadForwarder, and this daemon is responsible for connecting the serial forwarder on the TmoteConnect and grouping incoming sensor packets into larger UDP packets for forwarding to the server.As the mesh plane is the bridge between the sensor plane and the server plane, we have verified the full two-way data communication path starting from the sensors, through the base station, traversing the mesh network, and reaching the server. Currently, we are performing stress simulation tests on the buffering and forwarding mechanisms at the Clusterhead with thousands of sensors.3.4.The SUMAC server plane is the convergence point of the incoming data from all clusters of a single deployment. Each group of mesh network nodes report their data to a Mesh Portal. Mesh Portals in turn report the data to a single Mesh Server, that represents the bridge between the Mesh Plane and the Server Plane. The connection between the Mesh Server and Mesh Portal uses Secure Socket Layer (SSL), as it usually traverses third party long haul networks or the Internet. The Mesh Server receives streaming data from the Mesh Portals and stores it in a temporary table for provision to the client Web browser. It also forwards the packets to the database for long term storage through the DataStorage Component. The Comet  componenA central feature of the SUMAC Web interface is the map-based tool, shown in Through the map-based tool, users can click on the balloons on the map, which represent the sensor nodes at the selected location. Clicking on specific sensor nodes in the map opens a callout box that displays the current sensor readings at that node. In The graph tab in the callout also allows the user to view the recent sensor reading plotted versus time. To select a group of sensors in the map tool, users can also click on the “Select” button at the upper left corner of the map. The user then clicks, holds, and drags their pointer to create a rectangle around the target group of nodes on the map. The selected nodes then change color to yellow, and the ID's of these nodes appear in the box on the right titled “Selected Sensors”. Buttons for unselecting one or all sensors are also available in this tab.Once a group of sensor nodes are selected, the user can set the performance metrics for the selected nodes, by clicking the “Settings” tab in the right box of the main GoogleMaps tab. The “Settings” tab is shown in The “Threshold” tab enables the user to set maximum and minimum thresholds for each of the sensor readings. These settings are used by the sensors to detect anomalies in the monitored area. Whenever a sensor value exceeds its thresholds, the affected node stops aggregating data and wakes up its neighbors to report this potential event of interest see . The balThe “Threshold” tab also allows the user to visually verify and adjust the aggregation level in the network. By clicking and dragging the horizontal line up or down the network tree shown at the bottom right of After selecting all these parameters, the user is prompted to confirm these settings, after which the server sends the corresponding configuration requests to the selected sensor nodes.The SUMAC Web portal also supports query-based selection of sensor groups through a powerful yet intuitive interface within the “Group Selection” tab, on the basis of hop count, data resolution, sensed values, GPS coordinates, and sensor tags, as shown in C. Clicking the “Search” button generates a query to the Database through the Web Portal. The query results in a list of sensor node ID's to be sent back to the Web Browser and displayed in the “Results” box of the Group Selection tab. In the current example, the query returns three node ID's, which the user can add to the Web Portal or view the live sensor measurements.In the example of Real-time viewing of sensor data in the SUMAC Web portal is available by selecting a subset of nodes or a subset of sensors for selected nodes, either through the map-based tool, the group selection tool, or the “Measures” tab. The historical plot tab in the SUMAC Web interface enables the user to view stored sensor samples from any past interval. The user can specify the desired starting time for the plot, the time window into the past for which to show the readings, and the sensor node of interest. Clicking the “Draw” button generates the plot. 4.This section focuses on the performance evaluation of SUMAC's adaptive fidelity mechanism. To evaluate the adaptive fidelity mechanism, we have performed empirical testbed experiments at our lab at UCD's Computer Science and Informatics Centre using a single cluster of 20 Tmote Sky nodes. The same network scenario was used for all the experiments. The software implementation uses nesC and leverages TinyOS version 2.The 20 nodes are placed in an indoor area spanning two floors. Through TinyOS's low power listening feature, the duty cycle for all nodes is set to 30%. All nodes sample their sensors every 4 seconds and send their data towards the base station. Although our routing cost function from the CTM strategy supports energy and delay metrics, the experiments here use only ETX for better benchmarking by other TinyOS developers. Of course, the collected results would inform future experiments on setting cost function weights, mainly for managing the energy/delay tradeoff. Each test lasted for fifteen minutes. The network diameter varied between 5 to 6 hops, depending on interference conditions.To put the traffic reduction effects in perspective, The traffic and energy savings of our aggregation come at the cost of increased delay, which is il lustrated in 5.This paper has presented the SUMAC system for large scale sensor networks that targets monitoring of geographically dispersed sensor clusters. The SUMAC architecture introduces a mesh network overlay to extend the range of sensor clusters in order to cover larger geographic areas and to maximize user ownership of the data path. The data from the mesh network then reaches the server, which supports both real-time data access, visualization, and network control as well as running historical queries and analysis. All components of the SUMAC architecture have been implemented and integrated, and the verification of the end-to-end performance, robustness and scalability of the system has revealed that the system performs well, and additional experiments are ongoing to validate this further.SUMAC adopts an end-to-end approach to large scale monitoring applications, which includes the sensors, mesh, server planes. At the sensors plane, SUMAC introduces a suite of mechanisms for customization of the network performance through high level policies set by the user. These mechanisms include adaptive fidelity, which dynamically adapts the level of aggregation in the network to the current conditions, and a user-configurable cost function that determines routing paths in the network on the basis of application requirements. SUMAC also provides a feedback mechanism between adaptive fidelity and the cost function, to ensure that reconfiguration requests to the sensors jointly consider aggregation and routing topology, and adapt the network configuration autonomously to changing conditions without further user intervention.The mesh network plane serves as a medium-range overlay for the sensors. Gateway nodes between the sensor and mesh plane merge several sensor packets into a single UDP packet and forward through the Mesh network until the packet reaches a Mesh Portal, which then forwards the packet to the Mesh Server in the server plane.The SUMAC server design targets scalable data management for supporting tens of thousands of sensors nodes in a large number of clusters and deployments. The server provides live streaming data to the user through the Web browser, as described in Section 3.4., as well historical data storage and querying through a sharded, cloud-based relational database.Work on SUMAC is ongoing to refine the feedback mechanism that autonomously manages the network setting according to high level policies set by the user. We are conducting extensive testbed experiments to establish the relationships between aggregation, the cost function, and data delivery to the user.http://aws.amazon.com/ec2/) where we can simulate deployments of tens of thousands of nodes and the resulting delay behavior and resource usage of the server components. Our initial results indicate that a query to access historical data from the Web Browser for a network deployment of 1,000 nodes requires about 5 seconds delay on a Pentium 4 dedicated PC. Similar queries for a network of 10,000 nodes incurs a delay of about 5 minutes, and a network of 20,000 nodes results in a delay of about 20 minutes. We expect that our ongoing simulations will further reduce delay and reveal further trends which can be later generalized to data management and storage of large sensor deployments. Note that access to live real-time data streams is much shorter, in the order of a few seconds.We have implemented and verified the functionality of all the server components for 2 clusters of 20 sensor nodes each located at the Computer Science and Informatics Centre and at the Complex and Adaptive Systems Laboratory. We are currently running extensive simulations on the server to test its scalability and delay constraints. To perform these simulations, we are using the Amazon Elastic Compute Cloud service (In addition, we are improving the front-end interface by adding features to graphically indicate to the user the current state of nodes on the map. For example, features under development include color coding of nodes in the map which are aggregating, and another color to indicate nodes whose neighbors have detected an event of interest. The improved interface will also provide more advanced grouping functionality for easier manipulation of node configuration. It will also include an additional advanced option in to enable users to specify maximum cluster sizes, so that the server can allocate addresses accordingly to each base station.As a representative optimization mechanism developed within SUMAC, this paper has introduced the concept of adaptive fidelity by which users can set elaborate policies for specifying conditions for enabling aggregation and overriding it. The results in this paper have explored the energy/delay tradeoff and have determined that shifting the aggregation level from the 5th hop to the 1st hop from the base station can double the network lifetime at the cost of higher delay.The results also provide initial guidelines for enabling the system to autonomously manage the energy/delay with aggregation. The user can set bounds and requirements through the graphical interface on energy and delay requirements, and the system could then determine the appropriate aggregation level, and the weights of the cost function of the CTM, in order to optimally configure the network for achieving the user-specified performance requirements.


The genome of rabies virus encodes five proteins; the nucleoprotein, the phosphoprotein, the matrix protein, the glycoprotein, and the RNA-dependent RNA polymerase. Among these, the glycoprotein is the most important as it is the major contributor to pathogenicity and virus neutralizing antibody response. Keeping in mind that glycoprotein is the only protein exposed on the surface of virus and is thought to be responsible for the interaction with the cell membrane, it was attempted to target glycoprotein by a ligand polyethylene glycol 4000, which blocks its active site, as seen by molecular operating environment software, so that it may be possible to prevent the spread of virus into the host. The ligand polyethylene glycol 4000 was retrieved from Research Collaboratory for Structural Bioinformatics protein data bank by providing the glycoprotein sequence to the databank. In this study it was observed that the ligand was successfully docked on a major portion of antigenic site II of glycoprotein by mimicking the virus neutralizing antibodies. This knowledge may be important for the development of novel therapies for the treatment of rabies and other viral diseases in the future. Rhabdoviridae family, order Mononegavirales. The disease caused by rabies virus (RV) is fatal once clinical symptoms appear in the form of encephalomyelitis in several species of mammals including humans[Rabies, severe encephalitis of mammals, is caused by the members of the lyssavirus genus of the ng humans. Human rng humans. World-wThe genome of virus is about 12 kb that encodes five proteins; the nucleoprotein (N), the phosphoprotein (P), the matrix protein (M), the glycoprotein (G), and the RNA-dependent RNA polymerase (L). The viral RNA, which is always encapsidated by N, forms the ribonucleoprotein (RNP), which is the template for viral replication and transcription. The RVG6Previous research addressing the control of sylvatic rabies has focused on the development of vaccines. Although a vaccine for rabies exists, the disease remains a problem. Some very good vaccines are available, but economic reasons have kept them from being used in developing countries. In these areas, animal vaccines for rabies are available but are not widely used, a situation that allows the virus to spread among domestic animals, enabling a reservoir of the virus to exist intimately with humans. In spite of great advances in virology there is yet no cure of this disease. The present study was attempted to dock the pathogenic glycoprotein with identification of a suitable ligand in order to search a cure for this disease by development of novel therapeutics based on the information of docked molecule with virus glycoprotein.The modern docking programs/software packages e.g. MOE, Q-site finder, Ligand explorer, and Rasmol have been used to find the active site of the glycoprotein. The structure of ligand against this active binding site can be found by MOE. The exact conformation and configuration of the ligand can be calculated to find the best molecule with minimum binding energy and it can be used to develop potential drug molecules against the disease. If the glycoprotein can be targeted by a ligand which blocks its active site, it is possible to prevent the spread of virus into the host. This knowledge may be important for the development of novel therapies for the treatment of rabies and other viral diseases in the future.® Pentium 4, 1.8 GHz system having 256 MB RAM with the Microsoft Windows XP Pro 2002 operating platform was used. The software package MOE (Molecular Operating Environment)[In this investigation an attempt was made to carry out the docking of the ligand in to RVG protein with the following infrastructure. Intelironment) was usedRasmol program Research Collaboratory for Structural Bioinformatics (RCSB). Atomic coordinates were computed by the algorithm of the CORINA program (The structure of ligand polyethylene glycol 4000 (PE4) molecule was prepared using SMILE string from  program .For the preparation of receptor molecule, the protein sequence was loaded to the sequence editor tool and it was searched for template. The template 2CMZ was selected and used for the preparation of homology model. After loading the sequences these were aligned  by pairwThe energy of the protein molecule was minimized using the Energy minimization algorithm of MOE tool. The following parameters were used for energy minimization; gradient: 0.05, Force Field: MMFF94X+Solvation, Chiral Constraint: Current Geometry. Energy minimization was terminated when the root mean square gradient falls below the 0.05. The initial and final energy of protein were calculated  by GizMOE using MMFF94X force field with conjugant gradient method. The minimized structure was used as the template for Docking.Validation of modelled structure was carried out using Structure Analysis and Verification Server. StructuActive sites present in the protein were identified from the 3D atomic coordinates of the receptor using Q-SITE FINDER. It is an energy-based method for the prediction of protein-ligand binding sites.The binding of the ligand molecule with the protein molecule was analyzed using MOE docking program to find the correct conformation  and configuration  of the ligand, so as to obtain minimum energy structure. The parameters used for the Docking were, Total Runs = 50, Cycle/Runs = 15, Iteration Limit=10 000, Potential Energy Grid: ON, Annealing Algorithm: Simulated Annealing.The neuronal spread of rabies virus and hence the fusion of RV and host cell membrane should be controlled by blocking the active sites of the target glycoprotein. Conformational studies on the Asn194-Ser195-Arg196-Gly197 tetrapeptide, an essential part of the binding site of the rabies virus glycoprotein, indicate that the side chains of Asn and Arg could mimic the acetylcholine structure and is responsible for binding to the nicotinic acetyl choline receptor. If a liBy using our sequence of RVG from CVS strain (Accession number FJ979833) of rabies virus as a query sequence, many possible structures were generated by homology modeling using 2CMZ as a template. Among these the best structure (having minimum energy) was selected as a target of ligand PE4. The α-helix is represented by Red, β-sheet by yellow and loops by sky blue . ValidatThe energy of the protein molecule was minimized using gradient value of 0.05 and MMFF94X force field. The initial energy of the protein molecule was 934.59 kcal/mol and the final energy was 661.1023 kcal/mol. The minimized structure was used as template for Docking. The active sites in the present study were found using Q-site finder tool. The purpose of Site Finder is to calculate possible active sites in a receptor from the 3D atomic coordinates of the receptor. Such a calculation is useful to determine potential sites for ligand binding in docking calculations. The 3D structures of the active site were visualized by using tools like Rasmol. The amino acid sequence was retrieved using MOE software and several ligand molecules were designed against the active site by targeting these amino acid.The binding of the ligand molecule with the protein molecule was analyzed using MOE docking program . DockingThe pocket sequence of the active site was calculated by using site finder tool of MOE . From thThis ligand docks on a major portion of antigenic site II of glycoprotein. So this ligand may have a possibility to prevent the disease in patients suffering from immunodeficiency diseases. These docking studies promote us to build new ligand(s) which can completely block the viral pathogenesis by docking on the responsible sites.


Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such as chronic whiplash dysfunction, arthritis, and segmental degeneration. The natural inaccessibility of the spine, its complex anatomy, and the small range of motion only permit concise measurement in vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient's spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient's fluoroscopic sequence; intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted in about 0.2 degree for rotation and 0.2 mm for displacements. Neck pain is a common musculoskeletal problem experienced by the vast majority of the population , 2. AlteDetection of spinal instability (degenerative or traumatic) is based on accurate measurement of intervertebral kinematic . In partQuantitative measurements of segmental kinematics also find use in the evaluation of cervical arthroplasty, assessment of disc prosthesis, postsurgery followup, and so forth –11. In spite of their paramount importance in clinical application, accurate measurement of the intervertebral kinematic are hindered by the natural inaccessibility of the spine, the complexity of its anatomy, and physiology and the extremely small range of motion achieved at segmental level.Although most of the injuries and degenerative pathologies of the cervical spine are associated with reduced mobility and pain, there is no gold standard for the measurement of the kinematics of the cervical spine, not even for the measurement of its range of motion as a whole. Many techniques were proposed to measure spine kinematics –15. ThesThe simpler and less invasive methods  can only provide appropriate information about the entire cervical range of motion, but they are unable to accurately assess intervertebral motion (relatively large errors are associated with the sliding between skin markers and bones). Despite patient's radiation exposure, the radiological methods are currently preferred for many diagnoses. In particular, functional radiography is the clinical standard to detect segmental instability and decide whether to perform a surgery. Intervertebral kinematics measurements are currently based on functional flexion-extension radiography –18. HoweIt is worth mentioning that some authors suggested that disc degeneration may lead to abnormal location of intervertebral center of rotation while maintaining intervertebral translation and rotation within a normal range , 19, 20.Because of the indirect methodology and the physician manual selection on radiographies, intervertebral kinematic measurements suffer from large inaccuracy. This is particularly true for the estimation of center of rotation because small errors in vertebra location result in much larger errors on the estimation of the center of rotation.This study proposes a methodology for measuring the cervical intervertebral kinematics based on the processing of dynamic X-ray images able to provide objective measurements and a continuous description of spontaneous patient motion.X-ray fluoroscopy can allow time-continuous screening of cervical vertebrae during spontaneous neck flexion extension. Fluoroscopy is based on high-gain image intensifiers to strongly reduce the X-ray radiation dose and allow prolonged recording, but it produces images with a much poorer SNR than conventional radiography. The position and orientation of each cervical vertebra were estimated frame by frame of the fluoroscopy sequence by means of an opportune time-varying image processing. The intervertebral kinematics was then estimated by combining the trajectories of two adjacent vertebrae. Clinically, relevant concise measurements were also computed as well as the trajectories of the instantaneous centre of rotation. Motion data were interpolated and filtered with nonfitting splines to obtain a time-continuous description of the joint kinematics. Approximation error analysis was also performed.A 9-inche digital fluoroscopy device  was used for in vivo measurement. The X-ray tube parameters were adjusted for each subject; on average, they were set to 1 mAs and 50 kVp; the acquisition frame rate was set to 4 frame/sec; the focus-plane length was about 1 m. Digital radiological frames were acquired directly from the fluoroscopy device. Each image is memorized as raw image format (uncompressed), it is formed by 576 pixels, and luminance is encoded with 256 levels of gray, and the pixel size is 0.45 by 0.45 mm. The C-arm was set in horizontal position and the subject was put in, with his neck as close as possible to the image intensifier. Subjects were fastened to an adjustable-height chair by apposite belts in order to obtain shoulder stabilization. Subjects were instructed to spontaneously perform the maximum possible flexion-extension movement of their neck. Before recording, the subject became familiar with the assigned task in order to perform it correctly and enough slowly. The entire flexion-extension movement was performed in about 30 seconds. A calibration phantom was used to test for geometrical distortions and to measure image noise at different gray-levels.Since vertebra registration is mainly based on matching of bones edges (a derivation operation is required on the images), noise reduction of the fluoroscopic images is of paramount importance.In fluoroscopy, the numbers of X-ray photons are strongly reduced to keep patient's radiation dose acceptably low. The limited availability of photons per pixel generates the so-called quantum noise. Quantum noise is by far the most dominant noise in fluoroscopic images . QuantumAn accurate noise model , consideVertebra tracking was achieved by image template matching , 28. CerA template of each cervical vertebra was chosen by selecting portion of the vertebra projection that does not superimpose with adjacent vertebra along with the whole the patient's motion. In particular, the cervical vertebra template included the anterior vertebral body cortex and the spinolaminar junction particularly visible on the radiographic projection of spinous process (the area of the facet joints was excluded). The inclusion of the posterior process in the template , belonging to the template. It is worth noting that this expression for the cross-correlation index not only takes into account the product of the gradient magnitudes but also performs a scalar product between the gradient vectors. This improves accuracy of vertebra location with respect to the simple normalized cross-correlation image matching.The template matching was based on a particular image similarity index (GNCC), which combines the normalized cross-correlations of the horizontal and vertical gradients of the fluoroscopic image. The GNCC index was obtained according to the following formula:x-displacement, y-displacement, and rotation angle. This was obtained by progressively rotating the template with 0.1 degree increments and repeatedly computing the GNCC index. The coordinates of the global maximum of the GNCC index estimate the template displacement, while the angle corresponding to that maximum is held as the template rotation. Furthermore, 2D cubic interpolation of the GNCC function provided a subpixel accuracy for the vertebra displacement.Since each vertebra can be spatially translated and rotated in between two fluoroscopic images, the maximum of the GNCC index was searched in the three parameter spaces: x- and y-displacements and angles of rotation of a selected vertebra are available for all the frames of the fluoroscopic sequence. These three parameters, over time, completely describe the planar, rigid motion  of each cervical vertebra  were given byαUV is the angle of rotation of the upper vertebra, αLV is the rotation of the lower vertebra,  are the x- and y-displacements of the upper vertebra, and  are the x- and y-displacements of the lower vertebra.In particular, the intervertebral angle of rotation The intervertebral discrete-time data were interpolated by quintic nonfitting spline (similarly to ) providiFitting errors  of the spline interpolation were analyzed as they represent a quantitative index of the precision of the measurement made. The change-sign and Box-Pierce tests for whiteness were performed to ensure that the measurement errors were uncorrelated .Instantaneous centre of rotations (ICRs) were also computed but only for absolute angular velocities greater than 1 degree per second.Once the absolute cinematic is computed see , the intDiscrete measurements are depicted as dots, while the spline polynomial interpolations are shown as continuous lines. The extension phase develops in the time interval 3–9 s, while the flexion mainly at 21–24 s. Angular and linear velocities and acceleration were analytically calculated from the coefficients of the interpolating polynomial . Angle aP > 0.95. However, a larger error in correspondence of the flexion is visible, where the angular velocity resulted about in double of that corresponding to extension.x- and y-displacements, respectively.RMS values of the residuals resulted in 0.18 degree for the intervertebral angle and 0.15 mm and 0.14 mm for the intervertebral As an example, trajectories of the instantaneous centers of rotation of the segment C5-C6 are represented in During flexion , the ICRs move somewhat anteriorly, while in extension (3–9 s), move posteriorly. ICR trajectories result placed in the same location, they found in previous studies  for the Intervertebral kinematics closely relates to the condition of the soft tissue  intended to constrain segmental motion to maintain stability. Despite its importance, intervertebral kinematics is difficult to measure in vivo: direct measurements are not clinically viable, and little errors in estimation of vertebrae positioning may cause large relative errors in intervertebral measures. By means of fluoroscopy, it is possible to describe the whole progress of intervertebral motion in the plane of view. Template matching techniques can provide estimation of vertebra position in each frame. Then, spline interpolation provides both noise reduction and continuous representation of motion. Analysis of the measurement error shows their uncorrelation. It is worth to underline that the sampling frequency  has to respect Nyquist's theorem. Therefore, the movements of the patients should be enough slowly and smooth. At the moment, this technique seems inadequate to measure intervertebral kinematics  during vibrations , 35 and Previous studies  pointed Accurate measurement of intervertebral kinematics can offer an objective diagnostic tool to evaluate mechanical alteration of cervical segments and also can support evaluation and settings of different prostheses even during the implantation.


We report a rare case in which hemothorax occurred in addition to hemoperitoneum due to spontaneous rupture of hepatocellular carcinoma (HCC) originating from the caudate lobe of the liver. The case pertains to a 56-year-old female who was transported to our hospital for impaired consciousness due to hemorrhagic shock. Computed tomography (CT) demonstrated ruptured HCC originating from the caudate lobe and accompanying hemoperitoneum and right hemothorax. Hemostasis was carried out by transcatheter arterial embolization (TAE), and surgery was conducted approximately one month after TAE. In the present case, no lesions as possible sources of bleeding were observed inside the pleural cavity, and, moreover, the diaphragm had no abnormalities in the intraoperative findings, suggesting that blood from the ruptured tumor may have traversed the intact diaphragm to enter the right pleural cavity soon after the HCC rupture. However, to the best of our knowledge, no similar cases of HCC have been reported to date, and this case is thus believed to be very rare. This unusual phenomenon may therefore be strongly associated with the location of the ruptured tumor and the formation of a hematoma inside the omental bursa. We discuss the mechanism causing hemothorax in the present case and also review the previously reported cases of ruptured HCC complicated by hemothorax. Spontaneous rupture of hepatocellular carcinoma (HCC) is known to be a condition with poor prognosis. The liver is an organ inside the peritoneal cavity, so the rupture of HCC generally causes hemoperitoneum. Among these cases, few reports exist on the rupture of HCC originating from the caudate lobe,2 in whi3/μl, and the blood sugar level was 256 mg/dl, whereas all other items were within reference values. In addition, the tumor markers measured at a later date were α-fetoprotein (AFP) 814.2 ng/ml  and protein induced by vitamin K absence or antagonism factor II (PIVKA-II) 33 mAU/ml . A routine chest radiograph and a chest CT confirmed the presence of fluid collection in the right pleural cavity. An abdominal CT demonstrated a tumor and extravasation of the contrast medium in the caudate lobe of the liver as well as hemoperitoneum including a hematoma inside the omental bursa. The tumor seemed to be localized in the Spiegel lobe , but her blood pressure was characteristic of shock, namely at 70/40 mmHg, whereas her heart rate was regular at 71/min, her respiratory rate was 18/min, her body temperature was 35.5°C, and tenderness was observed in the epigastric fossa. A blood test indicated that hepatitis B antigen was positive, the hemoglobin was 11.5 mg/dl, white blood cell count was 11400/μl, the platelet count was 14 × 10Spontaneous rupture of HCC is not uncommon in Eastern countries, but there are not many reports on cases regarding ruptured HCC originating from the caudate lobe,2. When On the other hand, hemothorax is a very unusual presentation of ruptured HCC. The PubMed 1966-), Google Scholar, and Japan Medical Abstracts Society databases 1983-) were searched for articles published before May 2012 in English and Japanese languages that report on ruptured HCC accompanied by hemothorax. According to our search, only 16 cases of ruptured HCC complicated by hemothorax have been reported in the literature-19, and 66-, Goog3- were slocus minoris resistentiae around the aorta and/or around the esophagus and penetrating into the pleural cavity; This route taken by the pancreatic juice seems to be similar to the aforementioned one. However, no apparent hemomediastinum was present in our case, but this may have been due to the rupture of the mediastinal pleura occurring earlier than the blood coagulation inside the mediastinum, causing blood to flow into the pleural cavity.The diaphragm is a muscular tissue which separates the thoracic cavity from the abdominal cavity and has three openings: a caval opening, an esophageal hiatus, and an aortic hiatus. The caudate lobe is a section that anatomically comes in contact with the inferior vena cava, wherein in this case, the tumor and hematoma intensely retracted the inferior vena cava. Consequently, it is possible that the blood flowed along the connective tissue sheaths of the inferior vena cava, and, after entering the mediastinum through the caval opening, may have ruptured the pleura and flowed into the right pleural cavity. One example that may support this is a reported case in which the left gastric artery aneurysm ruptured, causing hemomediastinum and right hemothorax. Moreoveet al., who presented three cases of hemoperitoneum with secondary hemothorax in their article[There are other possible routes. Though a precise formation mechanism of hepatic hydrothorax is still unknown, it probably results from the passage of ascites through small diaphragmatic defects. These defects are generally located in the tendinous portion of the diaphragm which is said to be congenitally weak,23. No c article, althougOn the other hand, the lymphatic transfer through the diaphragmatic lymphatics, which is a well-known phenomenon in Meigs’ syndrome, may be Nevertheless, it is believed that the pressure around the tumor and in the omental bursa increased and blood passed through the diaphragm near the caudate lobe in the present case. This passage of blood seemed to occur soon after the HCC rupture, thus resulting in the coexistence of hemothorax and hemoperitoneum on the patient’s arrival at our hospital.Based on the aforementioned factors, at least three factors are necessary to explain the mechanism causing concomitant hemothorax in the present case. The first is the location of the ruptured tumor, that is, in the caudate lobe, which made it possible for the tumor to bleed into the omental bursa. The second is the presence of either the structural weakness or a defect of the diaphragm which may or may not have been congenital. The third is the large amount of bleeding that increased with sufficient rapidity to cause the pressure in the peritoneal cavity, especially in the omental bursa, which caused an unusual amount of stress to bear on a limited part of the diaphragm.The inside of the pleural cavity is negatively pressured, so it was predicted that the escape of blood from the peritoneum into the pleural cavity would make spontaneous hemostasis difficult. In actuality, when considering the amount of chest tube drainage, blood data, and abdominal findings, it is believed that after the patient’s admission into the hospital, most bleeding flowed into the pleural cavity in the present case. As shown in TableIt is said that TAE is not always effective and may not be easily performed for ruptured HCC in the caudate lobe because of the multiple tumor-feeding arteries. SuccessSpontaneous rupture of HCC is capable of causing hemothorax, which leads to very poor outcome. In the present case, the location of the ruptured tumor and the formation of a hematoma inside the omental bursa are considered to be strongly associated with the mechanism causing hemothorax. TAE is therefore believed to be an extremely useful treatment for hemothorax secondary to HCC rupture.Written informed consent was obtained from the patient for publication of this Case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.This research was approved by the ethical committee of the institution.AFP: α-fetoprotein; BCLC: Barcelona Clinic Liver Cancer; HCC: Hepatocellular carcinoma; CT: Computed tomography; JIS: Japan Integrated System; TAE: Transcatheter arterial embolization.Fuminori Ono and other co-authors have no competing interests.FO performed TAE and surgery. FO also drafted the manuscript. Other authors helped to draft the manuscript. All authors read and approved the final manuscript.