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Depth Based Multi-hop Routing (DBMR) is a variant of DBR which strives to reduce flooding and redundant packet transmission issues. In DBMR, multi-hop communication is employed during the forwarding process. Weighting Depth and Forwarding Area Division DBR (WDFAD-DBR) is proposed in . Void area occurrence is reduced by selecting next forwarder considering the depth of current node and expected next forwarding node. Weighted sum of depth difference of two hops have role to predict the holding time. There is a mechanism to change the forwarding region adaptively according to node density and channel condition. However neighbour node depth prediction error would lead to increase control overhead.
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| 99.9 |
Another depth based routing protocol is hydraulic pressure based anycast routing called: HydroCast . In HydroCast, firstly an opportunistic routing mechanism is proposed which reduces co-channel interference and ensures maximum delivery. Secondly a simple depth based void area avoidance mechanism is proposed. In HydroCast, void nodes maintain the recovery route to a node who’s depth is lower than its own depth. Then the void node packet is rerouted out of void using greedy forwarding approach. Opportunistic directional flooding is realized in Void Aware Pressure Routing (VAPR) . VAPR operation consists of two parts: enhance beaconing and opportunistic directional forwarding. Additional information such as sender depth, hop count to sonobuoy, sequence number and forwarding direction, is added to each node’s beacon in enhance beaconing phase. VAPR employs local greedy directional forwarding and utilizes a factor called surface reachability as forwarding metric.
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| 99.9 |
A Q-learning based cross layer approach is proposed in . Instead of hop count or depth, Q-learning routing path selection process includes traffic load, latency and queue length. Q-learning in distributed nodes ensures balanced energy consumption. Path Unaware Layered Routing Protocol (PULRP) with uniform deployment of nodes and with non uniform deployment of nodes is proposed in . It consists of two phases, the first phase called the layering phase, in which spherical layers of radius Rr with sink as layer 0 are formed and layering number is increased by each node. A node in layer l is to find forwarder from layer l−1 on the basis of transmission range and layering radius. The second phase is called communication phase in which potential relay nodes from each layer are selected for packet forwarding. Energy optimized-PULRP is an enhancement in which so called on the fly routing is considered to select relay node. E-PULRP incorporate energy in routing decisions, which was not considered in previous versions.
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| 99.8 |
An improvement in DBR named as EEDBR was proposed by A Wahid et al. in . In EEDBR, a sender node maintains depth, residual energy and ID information of its neighbours. Neighbours with smaller depth are selected and their list of IDs is included in data packet sent by sender. Receiving nodes hold the packet for holding time which is based on its residual energy Er. This may create problems as two nodes with same residual energy would have same holding time and could transmit at the same time. Moreover, EEDBR involves more number of hops in forwarding process and eventually brings increased delay and enhanced energy consumption. Hop-by-Hop Dynamic Addressing Based (H2-DAB) routing protocol is another localization free protocol. In H2-DAB, depending on the depth of nodes, different size of addresses are given to different nodes. Deeper nodes have large size address and shallower nodes have less size address. Connectivity based Routing Protocol (CRP) protocol is proposed in in which next forwarder node is directly related to Connectivity Index (CI) of neighbour node. where CI is defined as “number of neighbouring nodes closer to sink from a forwarding node”. Hop count based cross layer approach Channel Aware Routing Protocol (CARP) is proposed in . On the basis of recent history of successful transmissions a particular node is selected a next forwarder. CARP is hop count based protocol with varying power levels.
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| 99.7 |
Another localization free protocol, Adaptive Mobility of Courier nodes in Threshold-optimized DBR (AMCTD), is proposed in . In this protocol, forwarding of packet is improved and based on weight, which is function on depth, residual energy and some priority values assigned to them. An improvement in AMCTD named as improved AMCTD (iAMCTD) is proposed in in which different routing metrics are defined for different depth regions. These metrics include Localization free-SNR (LSNR), Signal Quality Index (SQI) and Energy Cost Function (ECF) and Depth Dependent Function (DDF). Moreover hard and soft threshold based simulation scenario is adopted in which a sensed event’s value greater than hard threshold is reported to sink. Moreover, authors employed courier nodes in for data gathering tasks. Here mobility of nodes start when certain fraction of total nodes have died, which is not efficient and full use of deployed resources. Holding time calculation process does not guarantee optimum value of holding time ensuring packet suppression and minimum end to end delay.
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| 99.25 |
Underwater wireless channel is severely affected by issues like multi path and fading. Signal to Noise Ratio (SNR) in UWSN is computed using passive sonar equation given below: (1)SNR=SL−TL−NL+DI where SL is source level of the target or noise generated by the target, TL is transmission loss in aquatic environment, NL is noise loss and DI is directive index. DI is the capability of receiving sensor to direct its antenna to avoid unwanted noise. SNR value must be greater than detection threshold (DT). All the above quantities are measured in dBreμPa. Transmission loss is given in following:(2)TL=10log(d)+α×d×10−3
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| 99.9 |
Thorp attenuation formula is used to calculate the totals attenuation A(l,f) given in the following formula. (4)A(l,f)=l×α(f)+k×10log(l) where l is the depth difference k is spreading coefficient with value 1.5 for practical spreading. The first term in Equation (4) represents the absorption loss while the second term is about the scattering loss. Passive sonar equation is used to find out Source Level (SL) which is used to find intensity of transmitted signal IT using the following formula. (5)IT=10SL/10×0.67×10−18 Transmission power of source is PT(d) calculated by the following equation. (6)PT(d)=2π×1m×H×IT Energy consumed in transmitting the k bits of packet is given as: (7)ETx(k,d)=2PT(d)×TTx where TTx is the transmission time in seconds. Speed of the acoustic signal is calculated by the following equation. (8)v=1499.05+45.7t−5.21t2+0.23t3+(1.333−0.126t+0.009t2)(S−35)+16.3z+0.18z2 Average end to end delay consists of two type of delays, calculated and explain in the following equations.
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study
| 99.8 |
Propagation delay: Propagation delay of an acoustic signal moving with velocity v between a sender i and receiver j separated by distance si,j is given by: (10)Dpi,j=si,jv The speed of acoustic signal v= 1500 ms−1 which is calculated using Equation (8). Sum of both type of delay constitutes to one hop delay. (11)Dni,j=Dtn+Dpi,j
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| 99.9 |
The sum of all hops delay equals to the total delay in a packet transmission from a certain node n to sink. It is also termed as end to end delay. (12)De2en=∑h=1hmaxDh The mean of all end to end delays gives average end to end delay. (13)De2e¯=∑n=1nmaxDe2ennmax
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| 99.9 |
Initially, we present a comprehensive analysis on the ranges of forwarding functions defined in previous protocols. We have identified problems in those forwarding functions and redefined them in next section. These redefined forwarding functions causes much better and systematic forwarding.
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| 99.9 |
Some important aspects of communication in underwater are neighbor selection, forwarding process (function) and holding time calculation. In order to predict the forwarding behaviour in network and its impact on holding time of nodes, it is convenient to have knowledge of domain and range of the forwarding functions. We have done comprehensive analysis on the respective forwarding functions defined in . The upper and lower bounds on the affecting metrics are given in Table 1. If the deepest layer of network is considered as reference level, hi = Dmax−Di gives the information about the height of node.
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| 99.7 |
DBR and other depth based protocols select neighbors on the base of depth difference Δd between the depth of previous (dp) and current node (dc). Holding time in DBR is also function of depth. Depth of broadcasting nodes can have range from; transmission range RT to maximum depth Dmax, which directly affects the holding time calculations. So this fails to explain the upper and lower bounds on possible holding time values while considering the delay and packet suppression tradeoff.
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| 99.9 |
EEDBR selects next forwarders on the basis of depth and residual energy. A node during its lifetime can have energy between 0 and Eo. Hence the range of forwarding function in EEDBR is between 0 to Eo. Weight based forwarding function in AMCTD is dependent on both remaining energy and depth difference and is given in following equations: (14a)W1=pv×ErDmax−Di where Er and Di are the residual energy and absolute depth of ith node respectively, pv is priority value which is a system parameter. Dmax is the maximum depth of underwater network. Weight calculation in AMCTD is updated and shifted more on remaining energy after 2% death of total nodes using following equation. (14b)W2=pv×DiEr
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| 99.8 |
For a unit value of pv all the forwarding function Wi calculated for AMCTD using Equation (14) has range in interval [0,∞). Holding time values depending on this forwarding function would vary abruptly and would not meet the optimum delay and reduced redundant transmission requirements. In AMCTD depth threshold dth is also updated after certain percentage of deaths, but weights are independent of dth and transmission range RT of nodes. Anyhow if there comes a lower bound on Er defined as min(Er≥1) and Di defined as min(Di≥1) it causes a significant change on the upper bound of all the respective forwarding function Wi as in Table 2. Since forwarding function of AMCTD are independent of depth threshold so maximum values are same regardless of dth. Dimensional analysis of each forwarding function describes that W1 and W3 are dimensionally equivalent (=Newton) while W3 is dimensionally reciprocal of W1 and W3.
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| 100.0 |
Forwarding function in iAMCTD is defined differently for different depth regions. SQI based forwarding function is defined in Equation (15a). (15a)SQI=LSNR×Erli In this equation li is depth difference between two successive nodes. LSNR is Localization free Signal to Noise Ratio defined in and given by the following equation. (15b)LSNR=PtA(l,f)×N(f) where Pt is transmitted power and (A()×N()) is the attenuation noise product factor, which is product of path loss and ambient noise. A(l,f) is the sum of absorption coefficient (Abc) and scattering coefficient (Sc) whose minimum and maximum values for different depth thresholds are given in Table 1.
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| 99.94 |
SQI based forwarding decisions are made if depth of the node is less than D1 which is top shallow region of underwater environment. This region involves more shipping activities. We have considered the statistical fact that variance is the measure of randomness in a set of observations. Careful analysis of SQI reveals that the range of output from this function is in the order of 105 as shown in Table 1. Such values must not be used directly as forwarding function as holding time calculated from these values would be very much incomparable, having high value of variance. So these values are needed to be made comparable before being used as forwarding function and holding time. This is explained as follows:
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| 99.75 |
If f = 30 kHz and depth difference l has range in the interval [dth=60,R=100] absorption coefficient has range [0.5969 dB, 0.9949 dB]. A(l,f) eventually comes out with in the range of [533.2,1245.4]. For f = 30 kHz noise N(f) comes out to be 1.12×10−5 using this, Attenuation Noise product range is calculated and finally LSNR is in range of [4.52×106,1.06×107] making SQI in range of [0,1.01×105]. This analysis is for dth=60 m but when dth is modified to 40 and 20 value of forwarding function for different nodes would be with larger values of variance. Direct use of values in this range is impractical and is needed to made comparable with other forwarding functions. Hence, in our solution linearized version of SQI is proposed to be used as forwarding function.
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| 100.0 |
ECF based forwarding function is utilized when depth of node is between D1=150 and D2=350. ECF is defined as: (15c)ECF=pv×ErDi where Di can be between D1 and D2. Value of Er is between 0 and 70 J and this results ECF values between [0, 70] which is very different from the range in SQI based forwarding function. ECF is also modified to include transmission range and depth threshold. If node is in deepest region of networks with depth greater than D2, DDF based forwarding function is employed using following equation. (15d)DDF=SQI×liDi
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| 99.4 |
Holding time is the time for a certain node during which it avoids any transmission and waits for the more appropriate forwarder to transmit the packet. Its value for each node is related to forwarding function of the underlying scheme. Difference of values of holding times for two successive nodes should be sufficiently large enough to avoid any collision. On the other hand very large value of holding time causes delay during transmission process. This delay may be negligible on a single node but in multi hop communication accumulated sum of all delays eventually causes larger considerable delay. So an optimum value of holding time is required to ensure redundant packet suppression and minimum delay. Holding time calculated for AMCTD and iAMCTD is given in following equation. (16)HT=(1−FF)×Htmax
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| 99.8 |
Holding time calculation depends on two factors Htmax and forwarding function FF. There is no appreciable explanation of Htmax which is termed as system parameter in previous literature. Whereas values for different forwarding functions can have range very much different from each other and these values are not even comparable with each other. Holding time calculated with these forwarding functions has very abrupt fluctuations for different depth nodes which causes increased delay in some regions and redundant transmission and collision in other depth regions. So there is need to have a relation providing comparable values of holding times. At least one must be able to predict the range of values to be assigned as holding time.
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| 99.5 |
Much of contemporary work in underwater sensor networks strives to have an effective routing mechanism ensuring decreased energy consumption and increased network lifetime. Each node strives to find a set of best possible nodes for the successful delivery of packet to surface sink. This situation (forwarding all the data to surface sink) would be appropriate if data generating nodes are only a fraction of all the deployed nodes. However in some applications in which all the deployed nodes are equally probable to sense and generate data packets, this approach causes increased broadcasting overhead. Each node has its own data plus it may has to forward the packets of other deeper nodes. This issue becomes prominent in deep underwater environment which involves comparatively greater number of hops in forwarding process. In this forwarding scenario node would be wasting its resources by causing congestion and increasing probability of collision of packets. Hence it is no more a wise option to broadcast all the generated data to surface sink(s). There is a need to decrease the broadcasting load on intermediate nodes to ensure optimum energy consumption. This becomes one of many motivation for the formulation of event segregation approach.
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| 99.8 |
One possible solution to decrease broadcasting load in some recent work is to deploy certain number of underwater mobile sinks (AUV or CN) which can move to the appropriate location for data collecting jobs. But it requires proper tour planning and coordination between nodes and sinks. Nodes may have to extra memory to hold the data for some time till the sink arrival. It would also cause increased delay and more susceptibility of congestion, collision and increased packet loss. It also requires a decisive parameter for a node so that it can be decided whether to forward to mobile sink or broadcast to shallower nodes. This broadcasting choice needs to be dependent on some decisive criteria. The event segregation approach provides an important criteria for this decision making step.
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| 99.9 |
Moreover conventional underwater protocols are generally planned for single application in which all sensing nodes are assigned the task of sensing a particular type of event and reporting to the floating sink. Occurring events have same occurrence probability in sensing field and require similar delay sensitivity for all nodes. These protocols provide no solution for diversified applications in which multiple events, with different tolerable delay requirements, can occur with different probability of occurrence. Moreover, generally UWSN are considered delay tolerant in which major constraint is energy consumption while it is assumed that there is relaxation in terms of delay. This is over simplified assumption as some events although with very less probability of occurrence may require to be reported much earlier than normal events. Event segregation approach considers this delay sensitivity associated with every node and plans more efficient approach for packet delivery.
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| 99.9 |
In this section our proposed protocol Event Segregation based Delay sensitive Routing is introduced and salient features of ESDR are explained. Directed Graph (digraph) theory is employed to serve as model for network topology in ESDR. Deployed nodes map to vertices and links between nodes represent edges of digraph. In digraph G=(N,A) vertices N are connected via edges or arcs A. Where N is the set of all deployed nodes. We denote the set of event sensing or data generating nodes by E, the set of broadcasting nodes by B and the set of courier nodes by C.
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| 99.9 |
There are some applications where only a fraction of all the deployed nodes are the data generating nodes, i.e., the members of E while the remaining nodes are the members of set B. While both the set E and B are disjoint and subset of set N. There are some other (regular data gathering) applications in which all nodes are equally responsible for data sensing and forwarding tasks. In such cases the set E is equal to set B and both sets are subset of set N.
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| 99.9 |
ESDR considers deep underwater environment which comprises of l m × b m × Dmax m. Initially N nodes having same initial energy Eo are randomly deployed. Each node is equipped with required sensing capabilities and a communication module. Each node is aware of its depth which is calculated using pressure sensors. We have considered the following assumption about the deployed nodes and network.
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| 99.94 |
It is assumed that the horizontal mobility of nodes is negligible while the vertical motion of nodes may change their depth. However, since it is a localization free scheme depending on the instantaneous depth, the change in depth is already accounted in the protocol operation. Hence the impact of mobility of water on nodes position is not taken into consideration. It is also assumed that each sensing node on the basis of value of sensed attribute can determine its type and delay criticality associated with it. One way to realize this assumption is by predefining some threshold levels of the being sensed parameter and then conditioning their type by the threshold values. After sensing the event a node determines its types by comparing the sensed value with the thresholds.
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| 99.9 |
Packet format for ESDR is shown in Figure 2. S.ID is sender ID, PSN is packet sequence number, depth of previous sender is kept in Dp, Er is the remaining energy of previous node. One important field is packet type which can be very critical, critical or ordinary.
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| 99.94 |
One of the unique aspects of ESDR is event segregation approach as it considers delay sensitivity associated with different types of events and assures efficient delivery to appropriate destination. We have devised a statistical model which considers different events occurring in sensing field. It is assumed that sensing node is able to determine the type of a particular sensed event and delay criticality associated with that event. In order to simplify the analysis occurring events are divided into three types: very critical, critical and normal/ordinary with the probability of occurrence pvc, pcr and pno respectively. Nodes with critical and very critical events are collectively termed as delay sensitive nodes and their data is termed as delay sensitive data. The nodes of each type of events are members of the sets defined as Evc, Ecr and Eno. Cardinality of these sets of node is given as:(17a)|Evc|=⌊pvc×|E|⌉ (17b)|Ecr|=⌊pcr×|E|⌉ (17c)|Eno|=⌊pno×|E|⌉ where sign ⌊⌉ represent the round off decimal value to nearest integer. The probability values are set as pvc=0.111, pcr=0.222 and pno=0.666 used in simulation section, which describe the number of nodes in each type of set. These values are assigned on the base of the assumption that more critical events are less likely to occur and vice versa. The values assigned to these probabilities ensure that; (i) the proportion of each type of nodes remain same throughout the network operation and (ii) the sum of all types of nodes is equal to the total number of data sensing nodes |E| as given by the following equation. (17d)|Evc|+|Ecr|+|Eno|=|E|
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study
| 99.94 |
Considering the above mentioned event distribution and the probability values, it is ensured that a specific node of its lifetime r units (rounds or seconds), ⌊66.6×r⌉ times it would sense ordinary event, ⌊22.2×r⌉ times it would sense critical event and ⌊11.1×r⌉ times it would be very critical event. This probability also ensures that at any time, ⌊66.6×|E|⌉ out of |E| event sensing nodes would be ordinary nodes, ⌊33.3×|E|⌉ nodes would be critical and ⌊11.1×|E|⌉ nodes would sense very critical event. However in this model nodes are selected randomly as critical, very critical and normal but it is ensured that the length of each set of node is not changed.
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| 99.9 |
In our proposed protocol, it is envisioned to formulate broadcasting mechanism for all types of nodes considering the delay sensitivity requirements of critical and very critical nodes. In our proposed protocol, we have planned the task dividing approach in which packets are forwarded either to the sink (floating on surface) or to CN based on their type and criticality requirements. Sojourn tour of CN can cause larger value of delay as data collected by CN is delivered to data center after its complete round trip. Hence it is ruled out to forward delay sensitive packets to CN. On the other hand, it is also inadequate to forward all the generated packets to surface sink(s) especially in deep underwater environments or in regular data gathering applications. That is why it is planned that delay sensitive nodes will forward the packets to surface sink using broadcasting and multi hop communication approach. Improved forwarding functions for critical and very critical packets are explained in upcoming sections. Forwarding function involves priority values pv whose value is increased to distinguish the forwarding of very critical packets from the critical packets. Normal nodes try to forward or broadcast their respective packets to CN. Hence CN mobility is planned and influenced as per normal nodes distribution and data generation. Broadcasting of normal node’s packets to CN can also involve one hop communication. If a normal node has packet to forward and it is not in the range of CN then it would check if any of its neighbours in the range of CN such that the packet is sent to that neighbor which relays the packet to the nearest CN. In order to accomplish this, normal nodes share their neighbor list with each other. Disjoint neighbors are updated by each node. Nodes with depth less than transmission range directly send their data to surface sink irrespective of their type.
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| 97.44 |
The forwarding function is the criteria for a node to decide whether to forward the delay sensitive packets or not. Forwarding decisions are made by each node irrespective of its own type. This means a normal node; which forwards its own packets to mobile sinks and do not broadcast to the surface sink, receives a delay sensitive packet it would be eligible for forwarding and forwards the packet if it comes out to be the best forwarding node. Likewise with iAMCTD, the forwarding function in ESDR is defined differently for different depth nodes. The SQI based forwarding function is redefined to ensure the comparable values of the forwarding function. (18)SQI′=log10(LSNR)×Er×RTli
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| 99.9 |
The SQI based forwarding function range is always in interval [0, 491.91]. The upper limit is far less than defined in previous schemes and is more comparable to the range of ECF and DDF based forwarding function. ECF based forwarding function FF is directly proportional to Er and inversely proportional to depth difference previous and current node depths Dp−Dc is defined below: (19)ECF′=[pv×RTDp−Dc]Er where RT is transmission range Er is remaining energy of a certain node. Forwarding function is directly dependent on remaining energy weighted by a factor given in square brackets whose range is described as follows: Neighbour selection process and depth threshold ensure that Dth<(Dp−Dc)<RT. Initially Dth is set to 60 m and is subsequently changed to 40 m and 20 m after certain percentage of deaths, and RT is constant =100. So for above mentioned values factor in square brackets in Equation (19) has range, from 1 to 1.66 for dth=60, from 1 to 2.5 for dth=40 and from 1 to 5 for dth=60. This eventually gives value of forwarding function always between 0 to 5×Eo which is comparable with the range of SQI′. DDF is also modified taking newly defined and linearized version of SQI. (20)DDF′=SQI′×liDi
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study
| 99.94 |
SQI based forwarding function is employed when depth of nodes is less than D1′=250 m, ECF based forwarding decision is used if node is in between D1′=250 m and D2′=750 m. Nodes with depth more than D2′ forward the packet using DDF based forwarding function.
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| 99.94 |
Courier nodes and AUV are employed for data gathering applications hence they are very crucial for underwater networks. Courier nodes can move vertically up and down and there is no facility of horizontal movement while AUV can be moved to any desired place with proper tour planning. However, there is no energy constraint on both of these devices. The mobility of courier nodes has not been exploited to its full potential in previous work. In most of the previous schemes, mobility of these nodes starts after certain percentage of death. Attaining the global knowledge of deaths and updating that to CNs itself has communication overhead. Additionally employing a resource in deep water and waiting for some certain level of calamity is not a wise option. Mobility of these nodes is needed to be exploited as per network requirements. So it is necessary to plan a better mobility pattern of CN to get full use of it. We have reconsidered the deployment and mobility of CNs. Moreover event segregation dictates and selects limited number of nodes to forward to CNs. Hence mobility of courier node is influenced by density of normal nodes and their rate of packet generation. In our proposed environment horizontal movement of CN is not required while vertical movement of each courier node is explained in two different ways. It is worthy to note that the mechanical aspect of the motion of mobile sink is beyond the scope of this work. Hence it is ignored that how and at what cost; these mobile devices move and what is the impact of underwater environment on this motion.
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study
| 99.5 |
In this type of mobility four courier nodes deployed on four different depths move in elliptical path. The centers of the four ellipse are given in Table 3. The motion is uniform in the sense that speed of each AUV remains same in its sojourn tour on elliptical path. By the term synchronized mean that phase relation between any two AUVs remains same throughout the tour as shown in Figure 3. In this figure two ellipses are shown by solid line and two by dashed line. The both AUVs of solid line ellipses move in same direction and remain in phase with each other, while both the dashed line AUVs are in phase, move in the same direction but opposite to the previous pair of AUV. The green dots in these figures represent the stopping points of AUVs.
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| 99.9 |
Elliptical path is planned in such a way that two ellipses have some overlapping region as depicted in Figure 3a. This allows better coverage to whole region and also facilitate to respective lower AUV to send its data to upper AUV. If the length of overlapping region on z axis is x, semi major axis length a is related with x by following equation. (22b)a=38x+125
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| 99.9 |
This relation reveals that for x=0, a becomes equal to 125 making length of major axis of the each ellipse to 2a=250. x is subsequently changed to 20 and 40 making a=133 and 140. The resulting ellipses are shown in Figure 4. In this type of mobility vertex length a=140 and co-vertex length b=50 are used. In order to calculate time period of a node it is necessary to have knowledge of perimeter (circumference) of an ellipse. There is no straightforward formula to calculate this and following infinite series is utilized. (22c)p=π(a+b)∑n=1∞0.5n2hn
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other
| 98.94 |
Mobility of CN is envisioned in such a way that CN1 is in phase with CN3 and CN2 is in phase with CN4. Initially CN1 and CN3 are at bottom end of their respective elliptical paths and CN2 and CN4 are at the top end of their corresponding elliptical paths. Every node moves in clockwise direction and reaches to next destination after time Ti/4. In this way a node in its one revolution has four points to stop each after phase angle of θ/2. Their motion is synchronized in such a way that when CN1 is about the top. Each node moves upward to their predefined destination, transfer data to next CN, and move back to initial stage. This motion pattern is synchronized in such a way that when a certain courier node is about to reach its destination next forwarder has just started its tour, first node find next CN to take its data to next CN.
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| 99.9 |
In this mobility pattern, nodes may accelerate or decelerate their speed on the basis of node density and data generated by the upcoming nodes. In this motion, nodes move up and down on a vertical line and their speed is influenced by node density and traffic generation rate of normal nodes. Courier nodes move in pairs as mobility of CN1 and CN3 is in one direction while CN2 and CN4 move towards other direction. This type of motion pattern ensures that these nodes must overlap and communicate with each other at the overlapping area. In order to illustrate the motion of a courier node c consider it is at position yc; its new position yt after time t can be determined by following equation. (25)dyc2dt2+1tdycdt+yc=1t2×yt
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other
| 99.9 |
In this equation left term (second degree differential term) corresponds the acceleration of courier node c. If this value is set to zero motion becomes uniform. For adaptive mobility of the nodes, this factor is dependent on the node density in the upcoming coverage region of courier node c. Upcoming coverage regions is double of the transmission range of courier node. This process is explained as follows: Initially a courier node c increases its transmission range to double of the present value and broadcast its arrival to its future region. Receiving nodes acknowledge with their depth information, rate of data generation and elapsed time since their previous transfer of data to any courier node. The courier node calculates the mean of the depth information of only those nodes which are currently not in its range. This provides that spatial information to estimate its next possible location in its sojourn tour. It also utilizes elapsed time data to calculate the allowed time to reach the estimated location. Using the depth difference between current depth and calculated depth; and remaining time node courier node c decides new velocity to reach at next location. Courier nodes keep the record of this location and utilizes it in its next sojourn tour. Hence location estimation of courier nodes is a gradually improving process. (26)yt=1Nd∑1NdDi where Nd represents total number of data generating nodes farther than current transmission range of courier node. Node utilizes elapsed time and average traffic generation of all nodes Nd to determine remaining time tr to reach new location. This remaining time tr along with new location distance yt are utilized to estimate the optimum velocity for next part of tour. (27)vc=yttr
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other
| 99.8 |
We have considered the deep underwater environment of volume 100 m × 100 m × 1000 m in which 224 nodes are randomly deployed. Analytical calculations are programmed in MATLAB using channel model equations. Generally in UWSN applications can be divided into two major groups on the basis of traffic generation. In some applications only a fraction of all deployed nodes are event sensing nodes which can generate data packets. The rest of the nodes are used to forward or broadcast that data. In other group all the deployed nodes can sense and communicate equally. The number of nodes in former group can be fixed and preselected based on their depth, or can be varying and adaptively selected based on some threshold value of sensed attribute. We have chosen the second group in our simulations in which all the deployed 224 nodes are data generating nodes and also act as forwarder to the received data packets. In simulation scenario each node is allowed to generate exactly one packet in one round.
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| 99.3 |
Simulation setup for random event generation of ESDR can be with many different possibilities. This number can be fixed or varying (as in present case we have chosen fixed number of node in each type). For example in this scenario nodes are selected randomly as critical, very critical and normal. This election is repeated after 100 rounds; however, it is ensured that total number of nodes in each type remains constant. So every time, a network ratio of very critical, critical and normal nodes is constant and it remains uniform for the whole lifetime of network. One can argue about varying numbers of delay sensitive nodes because who knows how many events can be delay sensitive. However, in order to simplify the analysis and simulation setup, so far a constant ratio of delay critical nodes is used.
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other
| 99.44 |
Throughput: Throughput is considered in terms of following two aspects: Packet sent to and received at sink: Number of packets sent to sink are dependent on type of application. In event driven or threshold sensitive applications some selected nodes are packet generator nodes, while in other type all the nodes have equal probability to generate the packet after some regular interval and sent to sink. Number of packets successively received at sink is less than the number of packets sent to sink.PDR—Packet Delivery Ratio: PDR is the ratio of number of packets received at sink to number of packets sent to sink. In ideal case where no packet is dropped PDR is equal 1, otherwise its value is less than 1 and greater than 0.
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other
| 99.9 |
Packet sent to and received at sink: Number of packets sent to sink are dependent on type of application. In event driven or threshold sensitive applications some selected nodes are packet generator nodes, while in other type all the nodes have equal probability to generate the packet after some regular interval and sent to sink. Number of packets successively received at sink is less than the number of packets sent to sink.
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other
| 99.94 |
Network lifetime is very crucial evaluation metric for the progress of any UWSN protocol. It is always required to prolong the lifetime with some efficient routing decisions considering the affecting parameters. Network lifetime is dependent on the energy consumption of deployed nodes which is calculated using Equation (7). Network lifetime of the underlying schemes is given in Figure 5. Stability period of ESDR is greater than iAMCTD after that iAMCTD shows somewhat stable behavior and there occurs rapid deaths in ESDR. This is due the difference of courier node mobility strategy between iAMCTD and ESDR. In iAMCTD courier nodes mobility is associated with the certain percentage of deaths. Hence courier nodes come into action as nodes have depleted a big portion of their energy and about to die out. Courier nodes data collection reduces large distance transmissions and bring somewhat stable behaviour in network. Anyhow this rescue mechanism could not prolong lifetime to very much extent. Halflife of both schemes is almost similar but after that iAMCTD nodes die out very rapidly whereas ESDR prolongs its lifetime to appreciable extent. Motion of courier nodes in ESDR is influenced by relative distribution and data generation of normal nodes. This motion is cyclic in nature in which, in each new cycle courier node utilizes record of its previous sojourn stay and estimates the better new position. This gradual improving mobility mechanism results into the deterrent behaviour of network. This type of mobility strategy may not stop the deaths but the lifetime of more important nodes (with higher rate of data generation) is prolonged while the death of lesser important nodes is tolerated for the overall benefit of network. iAMCTD nodes die out very early due to lack of network healing mechanism. Over all comparison proves that ESDR outperforms iAMCTD, EEDBR and DBR by considerable margin.
|
study
| 100.0 |
The lifetime of all the comparing schemes are different from each other. Hence lifetime cannot serve as a fair independent quantity for comparison of remaining evaluation metrics. So in order to have a common independent quantity we have taken certain but equal number of ascending samples of each respective metric, from the timeline of all the comparing protocol. Then these taken samples of the respective metric are plotted and compared. So samples in timeline of following figures actually refers to the equally spaced and same number of samples of each evaluation metric taken in from each protocol.
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other
| 99.9 |
Average end to end delay of the comparing protocol is calculated using Equation (13) and is plotted in Figure 6. ESDR performs better than its counterparts in terms of average end to end delay. Packet segregation causes significant decrease in delay. ESDR put comparatively lesser forwarding overhead on nodes and hence these nodes cause less retransmission and delay. It is also important to note that delay in DBR is lesser than EEDBR as the later selects forwarders on the basis of energy. Hence a node with lesser depth but more energy can become forwarder involving more number of nodes in forwarding process, which eventually accumulates into larger transmission delays. Delay comparison between different types of nodes in ESDR is given in Figure 7. Delay of normal packets is calculated as the sum of delay from a normal node to nearest courier node and the delay that would have been suffered by an acoustic signal from that courier node to surface sink. Average end to end delay suffered by normal packets is much more than the delay sensitive packets. Very critical packets are forwarded using higher priority values in forwarding functions so they suffer minimum average delay. Critical packet delay remains in between very critical and normal packets. Moreover as the delay in each type increases as the nodes start die out and large distance communication becomes more prominent.
|
study
| 99.94 |
Throughput of the entire network is measured in terms of total number of packets received at BS (Base Station). The comparison for total number of packets successively received at BS is shown in Figure 8. ESDR outperforms its counterparts in terms of throughput by considerable margin. This is because packet loss in ESDR is minimum due to packet segregation strategy. Two third of generated packets are sent to courier nodes available in underwater environment. There is very minute packet loss in this transmission since it requires maximum one hop communication. Only one third of total generated packets are broadcasted to surface sink. In previous protocol holding time value was depending on forwarding function which itself was having inconsistent values. In ESDR Improved forwarding function results into optimum values of holding time which eventually ensures decreased collision and lesser packet loss. So overall comparison, depicted in Figure 8 results into increased throughput of ESDR.
|
study
| 99.94 |
Another parameter to get the insight in terms of throughput is Packet Delivery Ratio (PDR) shown in Figure 9. PDR throughout the lifetime is calculated downsampled and plotted for all comparing schemes. Improved forwarding function and efficient adaptive mobility of courier node cause the increase PDR value for proposed protocol. Moreover since approximately 66% of the generated packets are transmitted to courier nodes which may cause delay but ensure reliable delivery of packets destinations resulting with improvement in PDR and throughput as in Figure 9.
|
other
| 98.7 |
Transmission loss is dependent on; number of transmissions that a packet undergo during multi-hop communication, attenuation loss of the signal, and bandwidth efficiency. It is calculated using thorp attenuation formula given in Equation (2). Major contribution to transmission loss is the spreading of the acoustic wave as it propagates away from the source for longer distances in underwater and cause greater transmission losses. Transmission loss for all comparing schemes is calculated and samples from whole lifetime taken in ascending order are plotted in Figure 10. ESDR performs best among all comparing protocol in terms of transmission loss as well. This is due the fact that a major part of data generated is transferred to patrolling sinks and is not suffered to long distances spreading losses. Moreover premature death of intermediate nodes in previous schemes cause increase in transmission loss for their respective protocols.
|
study
| 99.94 |
In this work, we have proposed a novel and energy efficient approach to accommodate delay sensitivity requirements with increased network lifetime for UWSN. We have formulated a strategy in which three types of events with different probability of occurrence are generated and their efficient forwarding is planned. Forwarding functions are analyzed and redefined and holding time calculation process is also improved. However, probability of occurrence for each type of event remains unchanged throughout the lifetime. In the future we intend to implement event segregation approach with varying provability of occurrence of normal, critical and very critical events. In this work, the generated events type (and hence node type) is randomly selected but in future type of event selection is intended to be related with it depth, remaining energy or any other relevant parameter.
|
study
| 99.94 |
Ginger (Zingiber officinale Rosc.) is an important cash crop which is widely planted in China, especially in Shangdong district. Ginger soft rot, caused by the soilborne pathogen Pythium myriotylum, is one of the most devastating diseases of ginger . The first symptoms of infected ginger are yellow leaves and collapsed shoots; below ground, water-soaked lesions appear on the developing rhizome . Under suitable conditions the rhizome rots rapidly and an infested field can be destroyed by the pathogen within a week .
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other
| 99.9 |
P. myriotylum is a hemi-biotrophic organism belonging to the Oomycetes class. It is widespread in India, China, Japan, Nigeria, Fiji, Australia, Sri Lanka, Hawaii and Korea . P. myriotylum is not only pathogenic to ginger but also to many other crops, such as cocoyam, bean, groundnut, tomato, tobacco and watermelon, resulting in significant decreases in yield and quality .
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other
| 99.94 |
Historically, only a few compounds have been registered for the control of P. myriotylum in China. Mefenoxam is the most frequently used fungicide in ginger seed treatment, which is effective in controlling ginger soft rot. However, excessive use of chemical pesticides has polluted the environment and harmed human beings and animals. Moreover, excessive use has resulted in pathogen resistance to pesticides. Because of these concerns, researchers are seeking new sources of materials to control Oomycete pathogens in crop production. Plant extracts are among the sources being investigated. Plant products have received global attention because they have constituents with novel structures; they are produced naturally, are biodegradable and generally do not leave toxic residues or byproducts which contaminate the environment; and they have less potential for developing resistance to pathogenic microorganisms [6–8]. Moreover, many oil extracts from plants are classified as GRAS compounds (“generally regarded as safe”) by the widely-regarded United States Food and Drug Administration.
|
review
| 99.8 |
Eupatorium adenophorum Spreng is a strongly invasive plant originally from Mexico but now a globally-widespread malignant weed. E. adenophorum may have spread globally when free of its natural enemy constraints (such as competitors, pathogens and predators). It releases inhibitors which interfere with the development of other species [9, 10]. In recent years, an increasing number of studies have shown that the terpenes in E. adenophorum comprise a class of important inhibitors which have allelopathic activity, antioxidant activity, antimicrobial activity, acaricidal activity, nematode activity as well as activity against other pests [11, 12–17]. However, there are no known reports of the ability of E. adenophorum oil extracts to control P. myriotylum.
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study
| 99.75 |
The present study identified the chemical composition of oil extracts from the leaves of E. adenophorum (termed ‘oil extracts’) and evaluated their effect against P. myriotylum in vitro mycelial growth and mycelial weight as well as in vivo activity. Moreover, its synergistic effects where assessed when mixed with synthetic fungicides on P. myriotylum in vitro. As a result of this work, a preliminary explanation was suggested for the possible mode of action of E. adenophorum oil extracts against the mycelium growth of P. myriotylum.
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study
| 100.0 |
Leaves of Eupatorium adenophorum were collected from Sichuan Province of China in May 2014. The plant was identified by Prof. Aocheng Cao at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing. Voucher specimen no. 20060712 was deposited at the College of Chemistry, Beijing Normal University. No specific permission was required as the plant material was sourced from uncultivated land, and the field studies did not involve endangered or protected species.
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other
| 99.94 |
The air-dried leaves were ground in a mill, and then passed through a mesh screen to obtain a uniform 40 mesh size. The powder was extracted with methanol for 12 h, and then treated by ultrasonic waves for 30 min at ambient temperature. The supernatant was evaporated to dryness under reduced pressure using a rotary evaporator. The crude methanol extract was dissolved in a small amount of methanol and extracted with ethyl acetate. The ethyl acetate extract was purified using XAD-2 macroreticular resin and eluted with MeOH: H2O: CHC12 (85: 10: 5). The elution liquor was concentrated and subjected to column chromatography over silica gel (200–300 mesh) which was first eluted with dichloromethane to remove non-polar compounds, followed by a mixture of dichloromethane: ethyl acetate 98: 2. The eluent was collected and found to be effective against P. myriotylum. After freeze-drying, a pale-yellow oily product was stored in an airtight sealed glass vial at 4°C for further testing. The yield of the oil extracts was 0.92%.
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study
| 100.0 |
The oil extracts were subjected to GC-MS analysis for identification of their chemical composition. GC-MS analysis was performed using a Thermo Scientific ISQ single quadrupole GC-MS, equipped with HP-5 MS capillary column (30m x 0.25mm x 0.25μm). For GC-MS detection, an electron ionization system was used (70eV ionization energy). The carrier gas was helium at a flow rate of 1 ml/min. Injector, ion source and MS transfer line temperatures were set at 250°C. The column temperature was initially kept at 50°C for 1 min, and then gradually increased to 280°C at a rate of 10°C/min, with a final 5 min of heating at 280°C. The injector volume was 2 μl in splitless mode. The chemical components were identified by comparing their relative retention time and mass spectra with those of standards and NIST library data of the GC—MS system.
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study
| 100.0 |
A poisoned food technique was used to evaluate antifungal activity against P. myriotylum . The oil extracts and two compounds (9-oxo-agerophorone and 9-oxo-10, 11-dehydro- agerophorone) were prepared using dimethyl-sulfoxide (DMSO, 0.5% v/v) as the initial solvent carrier followed by dilution with PDA (at about 50°C) to produce the desired concentrations of 40, 60, 80, 100 and 120 μg/ml. A 4 mm mycelial disk was cut from the periphery of 2-day-old cultures, placed in the center of each PDA plate, and then incubated in the light-dark cycle at 28 ± 1°C for 7 days. PDA plates treated with an equal quantity of DMSO were used as a negative control. Each treatment was repeated in triplicate. The mycelial growth (mm) in both treated and control Petri dishes were measured diametrically in two different directions using a vernier caliper. The percentage inhibition of fungal growth was calculated using the following equation when the mycelial mass had almost filled the Petri dish in the control: Inhibition(%)=[(C−T)/(C−4)]×100 Where C is the diameter of fungal colony in the control; T is the diameter of fungal colony in the treatment; and 4 is the diameter of the inoculum disc.
|
study
| 100.0 |
The effect of the mixtures on P. myriotylum was determined in radial growth experiments as described above. The treatment groups were: a) control group (PDA containing DMSO); b) E. adenophorum oil extracts in PDA; c) E. adenophorum oil extracts combined with a commercial fungicide (mefenoxam, mancozeb, iprodione or azoxystrobin) in PDA; and d) individual commercial fungicide in PDA. The interactive effect of the mixed components was evaluated using the formula provided by Colby [21, 22]: Exp=XAYB/100 In this equation, Exp = the expected mycelial growth inhibition rate of the mixture, XA = mycelial growth inhibition rate of E. adenophorum oil extracts alone, YB = mycelial growth inhibition rate of commercial fungicides (mefenoxam, mancozeb, iprodione or azoxystrobin) alone, Obs = the observed mycelial growth inhibition rate of the mixture. The synergism ratio (SR) is calculated as the ratio between Obs and Exp. If SR = 1, there is additive; If SR > 1, there is synergism; If SR < 1, there is antagonism.
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study
| 100.0 |
The inhibitory effect of E. adenophorum oil extracts on wet and dry mycelial weights of P. myriotylum were determined according to the method described by Dikbas et al. . The oil extracts were prepared using DMSO (0.5% v/v) as the initial solvent carrier followed by dilution with potato dextrose broth (PDB) to obtain test concentrations of 40, 60, 80, 100 and 120 μg/ml. Next, a 40 ml liquid medium containing different concentrations of E. adenophorum oil extracts was placed in each Erlenmeyer flask. Four 4 mm mycelial disks, cut from the periphery of the 2-day-old culture, were added to each flask. The control treatments (without oil extracts) were inoculated using the same procedure. The flasks were then incubated at 28 ± 1°C for 7 days in an incubator shaker. The fungal mycelia were harvested by filtration (separating them from liquid culture) and then washed three times with distilled water. The wet weight of mycelia was determined. The mycelia were then dried at 80°C for 6 hours, and the dry weight of mycelia was determined. Each treatment consisted of three replicates.
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study
| 100.0 |
Selected, healthy fresh ginger rhizomes were collected from Shandong Province, China. They were washed in running water, dipped in 70% ethanol for 5 min, and then washed 4 times with sterile water. The surface-sterilized ginger rhizomes were cut into thin slices (2–3 mm). Three concentrations of E. adenophorum oil extracts (100, 160, 200 μg/ml) were prepared with 0.5% (v/v) DMSO and 0.01% Tween-80. Each ginger slice was dipped separately in the oil extracts for 5 seconds and placed into a sterile Petri dish (9 cm in diameter), then inoculated with P. myriotylum by placing a 4 mm disc of mycelial material cut from the periphery of 2-day-old culture. All treatments consisted of three replicates with two ginger slices per replicate, and the entire experiment was repeated twice. The controls were prepared in similar manner using a mixture of 0.5% (v/v) DMSO and 0.01% Tween-80 instead of E. adenophorum oil extracts. All the Petri dishes were incubated at 28 ± 1°C for 4 days and the percentage of infected ginger was observed and recorded.
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study
| 100.0 |
In order to investigate the impact of E. adenophorum oil extracts on the hyphal morphology of P. myriotylum, a small amount of hyphae was taken from the edges of a colony grown on PDA treated with 80 μg/ml E. adenophorum oil extracts after ten days of incubation at 28 ± 1°C. The samples were then dipped in sterile water on a glass slide, covered with a glass cover slip, and observed under the microscope (Olympus BX63) at 400x magnification to determine any structural modifications. A control group, cultured without E. adenophorum oil extracts, was processed using the same procedures. Photographs were taken with computer-attached cellSens™ technology (Olympus Corporation, Japan).
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study
| 100.0 |
Ten-day-old fungal materials of P. myriotylum in PDB were amended with 0, 40, 60 and 80 μg/ml E. adenophorum oil extracts were used for the TEM observation to study the mode of action of oil extracts . The mycelium pellets were treated with 2.5% glutaraldehyde at 4°C, followed by rinsing with 0.1 M phosphate buffer (pH 7.2) and fixed with 1% w/v osmium tetraoxide solution. The fixed samples were rinsed with the same buffer three times. Afterwards, the samples were dehydrated using a series of ethanol solutions in the order of concentration 30, 50, 70, 80, 90, 95 and 100%. After dehydrating and embedding in Spurr’s resin, thin sections were cut and double-stained with uranyl acetate and lead citrate. The grids were examined with a New Bio-TEM H-7500 transmission electron microscope (Hitachi Company, Japan).
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study
| 100.0 |
GC/MS analysis resulted in the identification of twelve compounds representing 99.15% of the total oil composition (Table 1). The main components were oxygenated sesquiterpenes, such as 10Hβ-9-oxo-agerophorone (37.03%), 10Hα-9-oxo-agerophorone (37.73%) and 9-oxo-10, 11-dehydro-agerophorone (23.41%). Other components such as phytol (0.09%), 1-heptatriacotanol (0.01%) and geranyl linalool (0.45%) were present in smaller amounts.
|
study
| 100.0 |
The growth of P. myriotylum treated with E. adenophorum oil extracts was observed over seven days. The results showed that mycelia growth was reduced proportionately with increasing oil extract concentration (Fig 1). The mycelial growth was complete inhibited at 100 and 120 μg/ml concentrations after seven days of incubation. A concentration of 100 μg/ml of oil extracts was therefore regarded as the minimum inhibitory concentration for P. myriotylum. The response at 100 μg/ml is the same as at 120 μg/ml and is therefore not visible in Fig 1. The inhibition ratio of oil extracts against P. myriotylum was determined on the second day of incubation. At that time, mycelial growth was significantly inhibited at concentrations of 40, 60 and 80 μg/ml oil extracts, with reduction percentages of 47.27%, 65.31% and 69.86%, respectively (Fig 2). At the same concentration of oil extracts, 9-oxo-agerophorone and 9-oxo-10, 11-dehydro- agerophorone exhibited relatively poor control efficacy with inhibition of 0% - 33.82%.
|
study
| 100.0 |
Table 2 shows the relative growth inhibition of P. myriotylum to mixtures of E. adenophorum oil extracts and the synthetic fungicides: azoxystrobin, iprodione, mancozeb and mefenoxam. Importantly, all combinations of the oil extracts and these synthetic fungicides showed substantial synergistic effects on the mycelial growth of P. myriotylum. The synergism ratios (SR) ranged from 2.07 to 33.29 for the tested combinations of oil extracts and fungicides. Mancozeb alone (20 μg/ml) and a very low concentration of oil extracts alone (5 μg/ml) inhibited growth by 80.47% and 6.95%, respectively. However, the same concentrations of fungicide and oil extracts mixed together completely inhibited mycelial growth. Complete growth inhibition of P. myriotylum was also observed using a mixture of 100 μg/ml iprodione + 50 μg/ml oil extracts, compared with only 22.52% reduction in mycelial growth when 100 μg/ml iprodione was used alone. The oil extracts appear to synergise the effects of relatively low concentrations of mancozeb and iprodione fungicides against P. myriotylum.
|
study
| 100.0 |
The inhibitory effects of E. adenophorum oil extracts on wet and dry weights of mycelia were tested in PDB medium (Table 3). Various concentrations of the oil extracts were found to be effective in inhibiting P. myriotylum to form or increase biomass. The biomass of P. myriotylum was reduced to about half of that of the control at concentrations of 60 μg/ml oil extracts. The oil extracts completely inhibited mycelial growth and biomass formation of P. myriotylum at 120 μg/ml.
|
study
| 100.0 |
Fig 3 shows the results of tests on the antifungal activity of E. adenophorum oil extracts on ginger rhizome samples inoculated with P. myriotylum and incubated at 28 ± 1°C. The percentage of detached ginger rhizomes showing disease symptoms as a result of infection with P. myriotylum was reduced after first being treated with oil extracts from E. adenophorum at various concentrations The percentage of infected ginger was reduced significantly (P<0.05) compared with the control groups in samples that were treated with 160 and 200 μg/ml of oil extracts before being exposed to the pathogen. Ginger samples that were not treated with E. adenophorum oil extracts became completely infected with P. myriotylum. Fig 4 shows the results 4 days after inoculation with P. myriotylum. Decayed ginger (left) that was inoculated with the pathogen P. myriotylum (control), and healthy ginger (right) that had oil extracts (200 μg/ml concentration) applied before the pathogen was inoculated.
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study
| 100.0 |
The response of P. myriotylum to E. adenophorum oil extracts was observed using a light microscope at 400x magnification. P. myriotylum mycelia treated with 80 μg/ml oil extracts were compared with an untreated control. Compared with the control, hyphae exposed to the oil extracts were substantially modified (Fig 5). Untreated mycelia developed regular and homogeneous hyphae (Fig 5A). Compared with the control, mycelia treated with oil extracts produced larger diameter hyphae; a more heterogeneous distribution of the cytoplasmic matrix; more cytoplasmic granulation; less cytoplasmic matrix; and more decay of the cell wall (Fig 5B).
|
study
| 100.0 |
Ultrastructural changes to P. myriotylum as a result of exposure to E. adenophorum oil extracts were observed using TEM. The control mycelia of P. myriotylum grown in the absence of oil extracts showed the cell wall to be uniform; the cytoplasmic matrix was abundant; and organelle-rich cytoplasm was present that included mitochondria (m), golgi apparatus (g), vacuole (v), liposome (l) and nucleus (n) (Fig 6). All of those organelles have normal and uniform structures (Fig 6A–6C). In the presence of oil extracts at 40 μg/ml, the most conspicuous ultrastructural change observed was the lack of liposomes in the cytoplasmic matrix. The vacuoles, nucleus and mitochondria had the same appearance as the control hyphae, except there were fewer of them. At a higher concentration of 60 μg/ml, the oil extracts caused more obvious ultrastructural alterations: The cells were abnormally shaped, the cytoplasmic organelles were no longer present, cytoplasmic matrixes were absent, the cell wall was thinner and cell wall debris was observed (Fig 6G–6I). At a concentration of 80 μg/ml oil extracts, the cell ultrastructure damage was even more apparent as organelles and most cytoplasmic inclusions were completely absent, leaving only mostly empty cavity cells (Fig 6J–6L).
|
study
| 100.0 |
Improved living standards have resulted in the public demanding greater food safety. In the process of searching for plant protection agents with reduced risks to food safety, there has been an increasing attention on secondary metabolites of plants. E. adenophorum has been used by the Tamang tribes as an herbal medicine for treating fever and insomnia . Previous studies showed that E. adenophorum essential oil is rich in terpenes . This class of compounds defends many species of plants, animals and microorganisms against predators, pathogens and competitors . Terpenes in E. adenophorum therefore show great promise as plant protection agents.
|
study
| 85.94 |
This study used methanol and ethyl acetate to extract sesquiterpenes from E. adenophorum. Sesquiterpenes accounted for 98.57% of the oil extracts isolated using this method. A previous study that used a hydrodistillation method to extract sesquiterpenes in inflorescence oil and root oil of E. adenophorum reported a different composition of inflorescence and root oils . The use of different extraction methods might account for differences in the composition of the oils in the sesquiterpenes.
|
study
| 100.0 |
Previous studies by Kundu et al. and Ouyang et al. detected 9-oxo-agerophorone (10Hα and 10Hβ) and 9-oxo-10, 11-dehydro-agerophorone in ethyl acetate extract of E. adenophorum leaves, but this two compounds were given different names (cadinan-3-ene-2,7-dione and cadinan-3,6-diene-2,7-dione) by Kundu et al. . The oral toxicity of 9-oxo-agerophorone (10Hα and 10Hβ) and 9-oxo-10, 11-dehydro-agerophorone on mice was reported to be low . However, these compounds were effective against Rhizoctonia solani, Sclerotium rolfsii, Fusarium oxysporum and Macrophomina phaseolina, with EC50 values ranging from 89.74 μg/ml to 320 μg/ml . Moreover, the antifungal activity of sesquiterpenes on P. myriotylum had not been reported.
|
study
| 99.94 |
Our study indicated that E. adenophorum oil extracts have a marked inhibitory effect on P. myriotylum. The mycelial growth was completely inhibited when exposed to oil extracts of 100μg/ml. The antifungal activity of E. adenophorum oil extracts might be attributed to the presence of sesquiterpenes, because 9-oxo-agerophorone and 9-oxo-10, 11-dehydro-agerophorone are known to be strong antifungal compounds [13, 19, 28]. But the oil extracts have greater antifungal activity, possibly due to the effect of sesquiterpenes being synergised in the presence of other compounds.
|
study
| 100.0 |
Our laboratory bioassays demonstrated a strongly synergistic effect on P. myriotylum growth when E. adenophorum oil extracts were mixed with commonly-used synthetic fungicides (mefenoxam, mancozeb, iprodione or azoxystrobin). Moreover, the antifungal activity of oil extracts was higher than that of iprodione. The precise mechanism of this synergistic inhibition of P. myriotylum growth is not known. It is likely, however, that the synergistic effect is due to the components of the mixture targeting different sites in P. myriotylum mycelial cells . Mefenoxam inhibits ribosomal RNA (rRNA) biosynthesis , while mancozeb inhibits pyruvic acid oxidation. The antifungal activity of iprodione is related to its intervention effects on protein kinase in the signal transduction pathway. The mechanism of action of E. adenophorum oil extracts is not clear, but the mode of action of its major component—terpene—is speculated to involve membrane disruption by lipophilic compounds . The low-molecular weight and highly lipophilic compounds can easily diffuse across cell membranes to induce biological reactions . The synergy could conceivably be the result of a general increase in stress when different cellular processes are attacked simultaneously . Or, to be more precise, membrane disruption may allow a greater quantity of synthetic fungicides to reach the target sites successfully.
|
study
| 100.0 |
This study also showed that the E. adenophorum oil extracts reduced the biomass of the mycelium of P. myriotylum in a dosage-response manner. P. myriotylum was completely inhibited at 120 μg/ml in a liquid medium and by 100 μg/ml in a solid medium. This indicated that the oil extracts may be more effective in a solid rather than in a liquid medium.
|
study
| 100.0 |
In vitro studies on E. adenophorum oil extracts indicate their potential as a suitable antifungal agent against P. myriotylum. In vivo studies will be necessary to determine its efficacy as a botanical pesticide for the control of root rot in commercially-produced vegetables. The present results clearly demonstrate that E. adenophorum oil extracts significantly reduce decay in artificially inoculated ginger. However, higher concentrations of the oil extracts may be required in the field where pathogen growth may be favored by better nutritional and moisture conditions than those in the laboratory .
|
study
| 100.0 |
Although previous studies have highlighted the antifungal activity of plant extracts or essential oils from E. adenophorum, few have demonstrated morphological and ultrastructural changes to fungi. Light microscope observations of the microstructure of P. myriotylum revealed several mechanisms by which E. adenophorum oil extracts effect this fungal pathogen. Degenerative changes in cell inclusion and hyphal morphology were commonly observed, including cytoplasm granulation, lack of cytoplasm, increased hyphal diameter and cell wall decay (Fig 5). The changes indicated that the cell wall, cell membrane and cytoplasm may be the targets for E. adenophorum oil extract components. However, not all of these cellular components are separate targets. Some targets may be affected as a consequence of another mechanism being activated by the oil extract components . It is likely that attacks at multiple cellular targets will eventually lead to cell death.
|
study
| 100.0 |
E. adenophorum oil extracts had caused morphological and ultrastructural changes within P. myriotylum in a concentration-dependent manner. Results from the TEM are in agreement with light microscope observations. Oil extracts eliminated almost entirely cytoplasm and cellular organelles from hyphae. This suggested that the mode of antifungal activity of oil extracts of E. adenophorum is a result of the oil extracts targeting the fungal plasma membrane and endomembrane systems. Somewhat similar phenomena were observed in TEM studies by Tian et al. using Cinnamomun jensenianum essential oil on Aspergillus flavus, which is an oil abundant in terpenes . However, it was not clear whether E. adenophorum oil extracts destroy the membrane system directly by preventing membrane formation or indirectly by inducing new substance which damaged membrane integrity.
|
study
| 99.94 |
This work has shown that oil extracts from E. adenophorum possess fungitoxic activities that inhibit the growth of P. myriotylum, thereby increasing the possibility for using these oil extracts to control P. myriotylum in commercial crops in the future. The use of these oil extracts will also turn E. adenophorum from a weed to a valuable resource. Further studies are required to determine the stability of these oil extracts in the field and their phytotoxicity to a range of commercial crops.
|
study
| 99.94 |
Behavioral tests have been extensively used to study the visual recognition of animals. Recent developments in virtual reality (VR) tests of animal behavior has provided more consistent testing conditions, less human-animal interaction, opportunities to use a wider variety of experimental parameters, and more precise measurements of the perception of the tested animals. VR tests have been applied across a wide variety of disciplines from disease treatment in humans, to study of navigation and memory in primates, rodents, and even insects [1–9]. Despite the fact that many VR tasks use visual cues, most of the experiments in rodents which use VR probe the function of the hippocampus or memory formation/retrieval but not visual perception [10–17]. Currently, the most commonly used behavior test for visual function is the optokinetic test [18–21]. However, this test does not measure visual perception, but a reflex response. In addition, the optokinetic test is limited to testing spatial frequency or contrast sensitivity to horizontally drifting vertical bars . Many fundamental questions, such as how quickly an experimental animal can learn to recognize certain visual targets, how significantly the complexity of visual cues affects the training requirements and performances of animals, and how long the visual memory will last once animals are trained, remain unaddressed.
|
review
| 99.9 |
In this study, we developed a VR behavior test to characterize fundamental features of training mice for visual perception. We show that mice were able to recognize the designated visual targets after 9 days of training. However, their performance is significantly affected by the complexity of the visual targets. In addition, mice retained a memory of their visual recognition following a break in their behavioral training.
|
study
| 100.0 |
All animals used for this study were wild type C57BL/6 mice aged P25-P80. During the training period, mice were on a restricted diet and the body weight was monitored daily (see details below). All animal procedures and care were performed following protocols approved by the IACUC of the University of Utah in compliance with PHS guidelines and with those prescribed by the Association for Research in Vision and Ophthalmology (ARVO).
|
other
| 94.4 |
The VR system was constructed in a similar manner to the one described previously . Briefly, the system consists of a control computer (HP Pavilion HPE, HP Inc., Palo Alto, CA, USA) with dual displays, which runs a custom-written VR program. One monitor displays the visual targets to the mouse, while the other monitor displays the visual targets as well as other control parameters to the experimenter. The size of the monitor for mouse is 51.28 cm wide and the distance to the mouse from the monitor is 46 cm, which results in a field of view angle of 58.3°. A spherical treadmill is coupled to a high performance computer mouse (Logitech, G502, Silicon Valley, CA, USA), which feeds the movement of the spherical treadmill into the VR program. The VR program also drives a rewarding system to deliver a small amount of chocolate milk to the mouse when the mouse correctly identifies the rewarding target. An IR-sensitive video camera was used to monitor the performance of the mouse. Additionally, an audio cue was used to indicate reward delivery during the training to facilitate learning. The spherical treadmill, the monitor for displaying visual targets, the chocolate milk feeding needle, the speaker for providing audio cue, and the video camera are placed inside a light-tight box to reduce experimental disruptions (Fig 1, part A1).
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| 100.0 |
A) Overview of the equipment setup for the VR experiment (A1), the harness used to hold the mouse (A2), a mouse in a harness on the spherical treadmill (A3), and an image of a mouse performing in the VR environment (A4). B) Logic diagram of the VR program. C) Illustration of the conditions required for a mouse to receive a reward at the designated target (green). The shaded area represents where the mouse must be when facing the target to receive a reward. D) A view of both the colored/luminance targets (top), and the moving bars (bottom). Arrows are added to the moving bars to indicate the direction of movement.
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other
| 99.44 |
Prior to any training, all mice were individually caged, and given access to a running wheel in their home cage. Mice were then food restricted, and monitored daily. Food was given each day to maintain a body weight that was 85–90% of starting weight. After two weeks of food restriction, each mouse was dressed into a custom-made harnesses (Fig 1, part A2) under anesthesia of 2–3% isoflurane gas using a Vapomatic anesthesia system (Bickford Inc., Wales Center, NY, USA). A harness was affixed to itself in the back using staples, and then wrapped with tape to prevent harm to the mouse (Fig 1, part A3). Harnesses remained on the mice during the course of the training and testing. The following day mice were introduced to the VR set up by clipping their harnesses above the spherical treadmill (Fig 1, part A3). Training sessions occurred for each mouse twice per day, and each session was 30 minutes. Harnesses were removed at the end of training/testing under anesthesia of 2–3% isoflurane.
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study
| 99.94 |
The VR program was custom written using the Unity game engine (Unity Technology, https://unity3d.com/unity). The logic diagram of the program is outlined in Fig 1B. Briefly, the tested mouse was virtually placed at the center of a circular arena (2.4 meters in diameter) with a random orientation. Its X, Y position in the arena was continuously tracked using the input from the computer mouse. The tested mouse was then trained to navigate to a designated “Reward” target through moving the Styrofoam ball of the spherical treadmill. Once it properly arrived at the reward target and faced the center of the target for one second (Fig 1C), an audio cue played and a small amount (~100 μL) of chocolate milk was delivered through a feeding needle. The display of the arena was then automatically readjusted so that the tested mouse was placed back at the center of the arena (see S1 Video for a representative performance). If the tested mouse was unable to reach the reward target in 220 seconds, there would be a time out “Fail,” and the display was reset so that the tested mouse was replaced back at the center of the arena. The same procedure continued for 30 minutes, which constituted one “Session.”
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study
| 99.94 |
We used two different sets of visual targets for this study. One set consisted of uniform areas in the following solid colors: green, blue, light grey, and dark grey. These are hereafter referred to as the “colored” targets. The brightness of these objects was measured 46 cm away from the screen using an optical power meter (Model 371, UDT Instruments, Baltimore, MD, USA), and are as follows: green, 138.50 nW; blue, 87.31 nW; light grey, 224.80 nW; and dark grey, 7.37 nW. The second set of targets consisted of black and white bars moving in four different directions: down, up, left, and right. These are referred to as “moving bars” (Fig 1D, bottom panel). The width of the black and white bars (15.88 mm) was calibrated, so that when the mice were the farthest distance away (0.44 cycles/degree), or the closest (0.04 cycles/degree) it was within the known visible spatial frequency for C57BL/6 mice .
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| 100.0 |
Fig 2A shows the training/testing schedule for colored target and moving bar recognition. The entire training/testing procedure consisted of five phases. During the first phase (Acclimation), no visual display was provided from the target monitor. Mice were trained to maneuver on the spherical treadmill, acclimated to drinking from the feeding needle, and were rewarded with chocolate milk for moving forward on the spherical treadmill. During the second phase (Target Identification), the VR display was turned on, and the experimenter manually rotated the ball of the spherical treadmill to guide the mouse to the desired rewarding target. These two phases were used only for training, without data collection. However, to determine how naïve mice perform during the Acclimation phase, 5 mice were recorded during the Acclimation phase without any training for target identification. Data from these mice is used as a reference of untrained mice at the D1-D3.5 time points. After the initial two phases of training (Acclimation and Target Identification), the mice were allowed to navigate the arena without any human interference. For the next phase (Baseline), the performance of the mice was recorded as a baseline. Mice that did not drink chocolate milk from the feeding needle, or those unable to arrive at a rewarding target at least 6 times during a single session (30 minutes) were removed from further training and testing. About 50% of the mice were removed at this point, and they were not included in the final data analysis.
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| 100.0 |
A) An outline of the training schedule for the color/luminance recognition. B) Representative tracings (red dots and lines) of the movement of the mouse in the VR arena (black circle). The color-coded rectangles around the circle indicate the visual targets. B1 shows a representative tracing of a mouse that has just started training. B2 shows a representative tracing of a mouse that has been trained for 21 days.
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other
| 99.9 |
The remaining mice received additional training every day for 9.5 days (Advanced Training). The final measurement of their performance occurred during the last phase (Data Acquisition). To test the behavioral memory, three mice were given a break for 22 days after their training and testing. During this break harnesses were removed and the mice were allowed free access to food. Prior to memory recall testing, the harness was once again placed on the mice and food restriction was reinitiated. Mice were then placed back on the VR setup with no additional training, and allowed to locate the same reward target as before.
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study
| 99.94 |
Data is all presented as mean ± SEM in the text and figures (Igor Pro, WaveMetrics, Inc., Lake Oswego, OR). Student t-tests, Analysis of Variance (ANOVA), and Kolmogorov-Smirnov test (K-S) tests were used to examine the difference between means and distributions using Statview (Abacus Concepts, Berkeley, CA, USA). Fisher’s protected least significant difference (Fisher’s PLSD) tests were used for pairwise comparison of groups following an ANOVA test. Chi-squared tests were performed using equations coded in Excel (Microsoft Crop., Redmond, WA, USA). The level of statistical significance was set at 5%. In all figures, * indicates P<0.05; ** indicates P<0.01; and *** indicates P<0.001.
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study
| 99.9 |
We first examined how long it would take to train mice to perform a VR test to distinguish cues different in color and luminance. To determine if the mice can correctly recognize the designated color/luminance target, their movements were traced to quantify how often they arrived at each target during each session. When mice were initially introduced to the VR setup they tend to hold still, not moving enough to reach the periphery of the arena where the targets lie (Fig 2, part B1). At the end of the training/testing (phase 5), all mice wander freely around the arena looking for rewarding targets, even able to go directly to the rewarding targets from the center of the arena (Fig 2, part B2). After the first 9 days of training (Acclimation, Target Identification, and Baseline), mice can regularly arrive at the correct target (Fig 3A).
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| 100.0 |
The average number of rewards per session, the average distance traveled to reach a reward, and the average time required to reach a reward are analyzed as a function of training time. A) The average number of rewards per session of untrained mice (Acclimation, D1-D3.5), mice after Baseline training (Baseline, D6.5-D9), and mice at the end of training (Data Acq, D19-D21.5). An ANOVA test shows that the differences among these three groups are statistically significant (F(2,75) = 62.18, P<0.001). Fisher’s PLSD post-hoc analysis shows that the differences between all three pair-wise comparisons are statistically significant (Acclimation versus Baseline, P<0.001; Acclimation versus Data Acq., P<0.001; Baseline versus Data Acq., P<0.001). B) The average number of rewards and failures during the Baseline and Data Acquisition phases. A Chi-squared test shows that the difference between these two groups is statistically significant (F = 114, P<0.001). C) The average number of rewards per session as a function of training time. D) The total distance traveled per session during the Acclimation, Baseline, and Data Acquisition phases. An ANOVA test shows that the differences among these three groups are statistically significant (F(2,75) = 63.64, P<0.001). Fisher’s PLSD post-hoc analysis shows that the differences between all three pair-wise comparisons are statistically significant (Acclimation versus Baseline, P<0.001; Acclimation versus Data Acq., P<0.001; Baseline versus Data Acq., P<0.001). E) The average distance traveled per reward during the Baseline and Data Acquisition phases. A paired Student t-test shows that the difference between these two groups is not statistically significant (df = 18, t = 0.02, P>0.05). F) The percent of the total distance traveled that resulted in rewards during the Baseline and Data Acquisition phases. A paired Student t-test shows that the difference between these two groups is statistically significant (df = 22, t = 4.69, P<0.001). G) The average time required for reaching a reward per session during the Baseline and Data Acquisition phases. A paired Student t-test shows that the difference between these two groups is statistically significant (df = 18, t = 2.24, P<0.05). In all panels except panel C, the number in each column indicates the number of test sessions of 4 mice. Data are presented in the bar graphs as mean ± SEM in this figure and all of the following figures.
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study
| 100.0 |
Despite the mice being able to recognize the designated rewarding target within 9 days of training, the number of successful arrivals at the designated rewarding target in each session continues to increase with further training (Fig 3A–3C). Fig 3C plots the average number of successful arrivals at the designated target per session as a function of training time and shows that the number of rewards per session reaches the peak at day 13–14 of training, and then plateaus afterwards. Quantitatively, the average number of successful arrivals at the designated rewarding target per session was 0.03±0.03 for naïve mice during the Acclimation phase (D1-D3.5), 5.75±1.07 during the Baseline phase (D6.5-D9), and 18.29±1.95 during the Data Acquisition phase. An ANOVA test shows that the differences among these three groups are statistically significant (Fig 3A, F(2,75) = 62.18, P<0.001). Fisher’s PLSD post-hoc analysis shows that the differences between all three paired comparisons are statistically significant (Acclimation versus Baseline, P<0.001; Acclimation versus Data Acq., P<0.001; Baseline versus Data Acq., P<0.001). A Chi-squared test shows that the increase in the ratio of rewards versus failures significantly improved by the end of the Data Acquisition phase (Fig 3B, 1.5:1 at Baseline phase, to 7.6:1 at Data Acquisition phase, F = 114, P<0.001).
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study
| 100.0 |
To determine how mice improve their performance with training, we analyzed their movement in more detail. Our results show that, after the advanced training, mice traveled a farther distance during each session (Fig 3D) without reducing the distance for each reward (Fig 3E). The average distance per session was 24.39±3.49 meters (m) during the Acclimation phase. During the Baseline phase it was 216.81±24.98 m/session, and finally 343.80±29.75 m/session during the Data Acquisition phase (Fig 3D). An ANOVA test shows that the differences among these three groups are statistically significant (F(2,75) = 63.64, P<0.001). Fisher’s PLSD post-hoc analysis shows that the differences between all three pair-wise comparisons are statistically significant (Acclimation versus Baseline, P<0.001; Acclimation versus Data Acq., P<0.001; Baseline versus Data Acq., P<0.001). On the other hand, the average distance traveled for each reward was 14.59±1.45 m during the Baseline phase, and 14.24±1.14 m during the Data Acquisition phase. A paired Student t-test shows that this reduction is not statistically significant (Fig 3E, df = 18, t = 0.02, P>0.05).
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| 100.0 |
To determine if the mice spent a greater percentage of running distance arriving at rewarding targets after advanced training, we computed the percent of total distance that was used for receiving rewards during different phases. This distance was increased from 35.7±6% during the Baseline phase to 73.4±6% during the Data Acquisition phase (Fig 3F). A paired Student t-test shows that the difference between these two groups is statistically significant (df = 22, t = 4.69, P<0.001). The increased total running distance per session was accompanied by a reduced time required to arrive at each reward (Fig 3G). In average, mice spent 100.00±6.8 seconds (sec) to reach a reward during the Baseline phase. However, the time required for each reward was decreased to 75.07±5.9 sec per reward during the Data Acquisition phase (Fig 3G). A paired Student t-test shows that the difference between these two groups is statistically significant (df = 18, t = 2.24, P<0.05). All together, these results suggest that the training of the VR behavioral test seems to have two components, the training for visual recognition, and the training for maneuvering the treadmill. The training for the visual recognition is fast (9 days), but the training for controlling the treadmill requires more time and effort.
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| 100.0 |
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