Cardiac myocytes experience mechanical stress during each heartbeat. Excessive mechanical stresses under pathological conditions cause functional and structural remodeling that lead to heart diseases, yet the precise mechanisms are still incompletely understood. To study the cellular and molecular level mechanotransduction mechanisms, we developed a new ‘cell-in-gel’ experimental system to exert multiaxial (3-D) stresses on a single myocyte during active contraction.inside and outside the single myocyte during contraction in an elastic matrix.Isolated myocytes are embedded in an elastic hydrogel to simulate the mechanical environment in myocardium (afterload). When electrically stimulated, the in-gel myocyte contracts while the matrix resists shortening and broadening of the cell, exerting normal and shear stresses on the cell. Here we provide a mechanical analysis, based on the Eshelby inclusion problem, of the 3-D strain and stress (1) The fractional shortening of the myocyte depends on the cell’s geometric dimensions and the relative stiffness of the cell to the gel. A slender or softer cell has less fractional shortening. A myocyte of typical dimensions embedded in a gel of similar elastic stiffness can contract only 20% of its load-free value. (2) The longitudinal stress inside the cell is about 15 times the transverse stress level. (3) The traction on the cell surface is highly non-uniform, with a maximum near its ends, showing ‘hot spots’ at the location of intercalated disks. (4) The mechanical energy expenditure of the myocyte increases with the matrix stiffness in a monotonic and nonlinear manner.Our mechanical analyses provide analytic solutions that readily lend themselves to parametric studies. The resulting 3-D mapping of the strain and stress states serve to analyze and interpret ongoing cell-in-gel experiments, and the mathematical model provides an essential tool to decipher and quantify mechanotransduction mechanisms in cardiac myocytes. Cardiac muscle contraction generates mechanical force to pump blood, so the muscle cell experiences mechanical stress during each heartbeat. Excessive mechanical stress associated with pathological conditions, such as hypertension, volume overload, infarction, and asynchronous contraction, can result in cardiac remodeling and heart disease development load-free conditions. Investigation of the mechanotransduction mechanisms has been hindered by lack of techniques to control the mechanical load at single cell level, especially in the case of live adult cardiac myocytes. Pioneering studies developed techniques to apply longitudinal stretch to the single cell. Kohl and colleagues Previously, studies on cardiac excitation-contraction mechanisms were mostly conducted using myocytes under Precisely how mechanical stress in 3-dimensional (3-D) tissue affects the myocyte is still unknown. Under physiological conditions, the myocyte undergoes contraction and relaxation in synchrony with surrounding cells to pump blood against the pressure imposed by circulatory resistance (the afterload). In addition, under pathological conditions, such as infarction and asynchronous contraction , a myocyte may contract asynchronously against its neighbors and experience more severe and complex multiaxial mechanical stresses imposed by the surrounding myocardium.To investigate how multiaxial mechanical stress may affect the myocytes, we developed a cell-in-gel system by embedding live myocytes in a 3-D elastic hydrogel matrix. The gel is made of poly vinyl alcohol (PVA) and tetravalent boronate-PEG crosslinker inside and outside the myocyte when it contracts within an elastic matrix. The model provides the interior (cell) and exterior (matrix) displacement fields that can be directly compared and calibrated to experimental imaging measurements in terms of fractional shortening of the myocyte and micro-bead displacements in the matrix. With knowledge of the gel stiffness, the model can be used to calculate the multiaxial stress state inside the cell, the traction distribution on the cell surface (adjacent to chemotransduction sensors), the stress and strain distributions in the gel (if of interest), and the elastic energy expended by the cell. The remainder of this article provides the theoretical development of the model, a parameter study to highlight general trends and gain insights for cell-in-gel experiments, and a discussion of implications and extensions.In this article, we provide a 3-D mechanical analysis of the single myocyte contraction in-gel. The purpose is to provide a quantitative tool to guide, analyze and interpret ongoing cell-in-gel experiments, which are ultimately aimed at deciphering the cellular and molecular mechanisms of mechano-transduction in the beating heart. Those experiments are generating a large body of data, and the results will be reported elsewhere. Here, we focus on the mathematical model that provides a foundational analysis tool to quantify and map the 3-D mechanical fields inclusion problem where a subregion (the inclusion) of an infinite, homogeneous, linear elastic solid undergoes a spontaneous change of shape (transformation strain). Since the inclusion is constrained by the surrounding elastic matrix, a residual state of stress is created inside and outside the inclusion and the inclusion is restrained from achieving its new, stress-free, configuration. Likewise, we envision the cardiac cell attempting to contract, but its deformation is constrained by the surrounding gel and only a partial contraction strain is achieved. Eshelby went on to address the inhomogeneous inclusion problem, where the elastic moduli of the subregion are different from the surrounding matrix, by formulating the equivalent inclusion problem. The usual purpose is to calculate the homogenized elastic properties of a composite material Of interest is the boundary value problem of a single beating cardiomyocyte embedded in an elastic hydrogel of infinite extent see . The theOur key assumptions are:The cell is treated as a homogeneous ellipsoidal inclusion.The cell and gel behave as isotropic linearly elastic solids.Displacements, rotations, and strains are small, allowing linearized kinematics.Cell contraction is isovolumic (isochoric) and occurs uniformly throughout its volume.The cell membrane is adhered to the gel.Remote boundaries of the gel are load-free.The analysis applies to a single myocyte in gel, without mechanical interactions with other cells.Most of these assumptions, except perhaps the first two, are reasonable considering the cell-in-gel experiments we have mind. We recognize that typical cardiomyocytes are not ellipsoids (often irregular brick-like shapes) and the cell and/or gel may not be linearly elastic (likely somewhat nonlinear and viscoelastic). For now, however, we are content to accept these assumptions, since they greatly simplify the analysis and give a useful first-order analysis that can be extended later if needed.A fixed Cartesian frame is used with orthonormal base vectors The boundary value problem of interest is a solid body of infinite extent , we need the higher derivatives of From here on the Using again eq. (12) and taking derivatives of eqs. (13a) and (13b) givesellipsoidal inclusion all derivatives of the Following Knowing only Computing the required derivatives of Differentiating eq. (12) by From eqs. (14) and (21a), the first derivatives of the From eqs. (21) and (22), and the fact that M-quantities in eq. (17b) can be writtenUsing eqs. (14), (21a), and (22), derivatives of Finally, the displacement and strain fields are computed by eqs. (9a) and (9b), and the stresses are found fromThe deformation of biological materials and most soft polymers can reasonably be considered as isochoric  is multiplied by Specializing to the form of So far, we considered the case where the elastic properties of the inclusion and matrix are the same , we started with the traction (Equation (43a) gives the total strain energy in the system, which is potentially useful to determine energy requirements for the cell. Equation (43b) is the strain energy in the matrix, which is just the mechanical work done on the gel by the cell. Both results are valid for the homogeneous and inhomogeneous inclusions, where With the general analysis complete, explicit calculations are presented below. The fact that an analytical solution is available is fortunate, and this readily facilitates easy parametric studies of the problem. In particular, we are interested in predicting the constrained strain state in the cell, knowing its load-free contraction From here on it is convenient to take the principal axes of the cell as Although cell to cell variability exists, a typical healthy, ventricular myocyte is about Assuming the shear moduli of the cell and the gel are the same  and using eq. (28) with the geometry of our nominal cardiomyocyte, the calculated strain state in the constrained inclusion isconstrained axial strain to load-free axial strain during cell contraction. For the homogeneous inclusion the knockdown is about The strain values above, when normalized by the axial  transformation strain stress larger. One could then reinterpret For our canonical myocyte, the knockdown factor is 0.2. Note that the assumption behind this calculation is that the inclusion is a passive elastic object with The stress state inside the homogeneous inclusion, corresponding to eq. (44), is calculated to beThe non-zero values above are in fact normalized principal stresses, showing that the longitudinal stress is tensile The surface traction distribution is also of importance, considering that various signaling molecules that reside near or on the cell membrane. While the stress state is uniform within an ellipsoidal inclusion, the traction distribution on its boundary is not. The traction vector Correlating the spatial distribution of strain and stress with cellular architecture provides important insights on how mechanical load is supported by cellular structures and how mechanical stress is transduced by macromolecular complexes to affect biochemical reactions. One important finding of our analysis is that the stress state within the cell is uniform, at least from a continuum viewpoint. Hence myofilaments are expected to bear a uniform distribution of the strain and stress throughout the entire cell during contraction. The myofilament is composed of the thick myosin filament, the thin actin filament, the titin filament, and associated proteins. Upon excitation, the thick filament pulls on the thin filament to generate active contraction of the myocyte, while the titin filament provides passive elastic constraint during both stretch and contraction Another important finding is that the traction distribution on the cell surface is highly non-uniform. This is expected to generate non-uniform strain and stress in the extracellular matrix, the cytoskeleton network, and the intercalated disk Selected field quantities  in the inclusion and matrix are shown in For the case when matrix mechanical properties are different from the inclusion (the inhomogeneous inclusion) the calculated knockdown factors are provided in For our baseline case, Until now, we have provided dimensionless plots in the interest of generality. Given that we have a linearly elastic solution, the strains are proportional to external sources to the cell so we take this baseline energy to be zero. According to this definition, the total strain energy rises monotonically as An interesting outcome of our analysis is the ability to calculate the mechanical energy expended by the single myocyte during contraction. The total elastic strain energy by eq. (43a), using eqs. (44) and (45), is In the case of our canonical myocyte contracting in a gel of the same stiffness, the total strain energy estimated from our mechanical analysis is about The mechanical stress states are certainly different in a myocyte in the cell-in-gel system versus the carbon fiber system, but considering the finite stores of ATP in the cell it seems reasonable to compare the two on an energetic basis. In non-biological mechanical systems, the elastic energy is often successfully used to compare structural systems with very different stress and strain states, and the energy turns out to be the unifying measure for many failure analyses. Accordingly, the energy  puts a given myocyte subject to various boundary conditions on a similar footing, thereby allowing a comparison of their mechanical behaviors. Also, the energy density should be less sensitive to the broad variability in myocyte shape than other measures (such as stress and strain fields). The fact that the energies are similar between the cell-in-gel model and the carbon fiber experiment seems to support this view, but direct comparison to experimental data is needed to provide more convincing proof. We note that the total energy required for myocyte contraction should include not only this mechanical energy but also the energy used to maintain metabolic homeostasis, ionic homeostasis, CaThe current analysis provides an analytical solution that readily lends itself to parametric studies. As shown, it gives a useful first-order analysis of the magnitudes of constrained strains, stresses, and energies involved, which provides guidance for using cell-in-gel experiments to investigate mechano-chemo-transduction of myocytes under various mechanical loads.As noted above, the results above were largely provided in dimensionless form to keep the results general and to investigate trends. Specific values, where needed, were cited only for illustration purposes. In particular, the value As mentioned previously, our mechanical analysis treats the myocyte as an idealized elastic, yet contractile, entity rather than a real live myocyte that can actively regulate its contractile force via mechano-chemotransduction, which contributes to the Anrep effect where increased afterload enhances contractility While initially motivated by the cell-in-gel experiments, we can imagine some in vivo situations where our mechanical analysis might also apply. In a normal heart where myocytes essentially beat in synchrony, the strain field is relatively uniform, albeit a transmural gradient. However, under pathological conditions asynchronous contraction  and inhomogeneous inclusion  occur in the myocardium, and these can be readily simulated by the current analysis. For example, the ‘inverse’ of the current problem is that of an infarct scar in a contracting myocardium, where the local stress field around the scar would be important to know. Now the interesting region is outside the inclusion rather than inside. Our mechanical analysis still applies, but now with the sign of Regarding limitations, the most arguable assumption of the current analysis is that of the ellipsoidal shape of the inclusion. If one wants greater fidelity in modeling the actual shape of a particular cardiomyocycte the current analysis can be extended by the algorithm given in Rodin Another extension that might be useful is to consider the case of linear viscoelastic cells and/or gels. For certain boundary value problems, of which the current analysis is one, the “Correspondence Principle of Viscoelasticity” inside and outside the myocyte. Our analyses reveal the following phenomena of particular patho-physiological importance. The fractional shortening of myocyte is dependent on the geometric dimensions of the cell and the stiffness of the surrounding matrix. A slender or softer cell has less fractional shortening. The stress state is uniform within the cell and can be approximated as uniaxial, considering the large ratio Mechanical stress is known to have significant impact on the heart function and disease development. What remains a major mystery is the cellular and molecular mechanism(s) that transduce stress to affect cardiac myocytes. As a necessary step towards understanding the mechanical stress effects, here we provide a 3-D mechanical analysis of a single myocyte beating in an elastic matrix that simulates the mechanical environment in myocardium under certain pathological conditions. The general analytic solution facilitates parametric studies of the problem and provides a quantitative mapping of the mechanical strain and stress


It is an effective strategy to use both genetic perturbation data and gene expression data to infer regulatory networks that aims to improve the detection accuracy of the regulatory relationships among genes. Based on both types of data, the genetic regulatory networks can be accurately modeled by Structural Equation Modeling (SEM). In this paper, a linear regression (LR) model is formulated based on the SEM, and a novel iterative scheme using Bayesian inference is proposed to estimate the parameters of the LR model (LRBI). Comparative evaluations of LRBI with other two algorithms, the Adaptive Lasso (AL-Based) and the Sparsity-aware Maximum Likelihood (SML), are also presented. Simulations show that LRBI has significantly better performance than AL-Based, and overperforms SML in terms of power of detection. Applying the LRBI algorithm to experimental data, we inferred the interactions in a network of 35 yeast genes. An open-source program of the LRBI algorithm is freely available upon request. Exploring the structure of Gene Regulatory Networks (GRN) is a key element in understanding gene functions, especially in some complex diseases Several models have been applied to describe the GRN. An intuitive and frequently applied method is to model the GRN as graphs In the last decade, Structural Equation Modeling (SEM) In this paper, we also study the gene regulatory networks with SEM model using both genetic perturbation data and gene expression data, and transfer the SEM to a Linear Regression (LR) model through matrix transformation. In this transformation process, regulatory information in GRN will not be lost. Instead of ML approaches or classic Lasso methods, we propose an approach to infer the networks via the LR model by using a Bayesian method (LRBI). Simulations show that our LRBI algorithm is effective and reliable, and offers significantly better performance than the AL-based algorithm. Compared with SML, LRBI has significantly better performance in terms of power of detection, but has slightly worse performance in false discovery rate. LRBI also has the advantages that the estimation of the initial parameters and the consideration of the data sensitivity are not needed.We consider We assume that there is no self-loop of each gene, so that all diagonal entries of With the predetermined eQTLs matrix Since all the unknown parameters are in By the definition and the structure of (3), we can infer the parameters row by row. Therefore, the problem can be decomposed intoIn (4), the parameters that need to be inferred are In gene regulatory networks, most entries of With known Rewriting (5) leads toWe assume that where The likelihood iswhere According to the prior distribution in (7∼8) and the likelihood in (9), the joint posterior distribution (10) can be written aswhereandMore details of the derivation can refer to . Thus, we modify the calculations of We can sample the posterior distributions in (12) and (13) to constitute an iterative process. Since the values in (14)∼(16) are all determined, the parameters of the Gamma distribution in (12), The combination of  and  forms an iterative process. We execute this iterative process with a sufficient number of times, and until a steady state is reached. A sequence of sets of The sketch of an algorithm is as follows:Input the eQTLs matrix For Calculate Repeat:Get the sample of Get the sample of Calculate Calculate If End forMore details of the algorithm implementation can refer to the software package F1.It should be noted that it would be better to choose the second half of the samples and average them to get accurate result. The reason is that at the beginning of the iteration, the gap between the estimated Let Logsdon and Mezey Firstly, we carried out simulations following the setups in We generated cyclic or acyclic networks for simulations, and used LRBI to infer the parameters of the simulated networks. For cyclic networks,LRBI can obtain the steady-state solutions naturally. By inference, the steady regulatory relations can be got, if some cyclic regulatory relations among genes existed.Due to the inference characteristic of Bayesian methods, the estimated parameters are not regressed to zeros as in Lasso methods. Therefore, an edge from gene Simulation results for the setups described above are shown in Secondly, we simulated two types of directed cyclic gene networks: one with 10 genes and the other one with 30 genes. Averaged Thirdly, we simulated the impact of different decision thresholds on performances. We used a bigger network We continued the simulations with a even bigger network with Finally, we evaluated the impact of noise levels on the performance of LRBI. Here, we used networks with We have stated that LRBI cannot infer parameters to zeros automatically, but can infer them with high precision. In most of the simulations we conducted, the decision thresholds are 0.05. That is to say, if the value inferred is lower than 0.05, the entry is considered to be zero. This implies that the numerical difference between the inferred value and the original value is less than 0.05 for most of the entries in regulatory networks. We define that numerical difference as Here, we applied LRBI to infer the gene regulatory networks using the gene expression data and the genetic makers, which were assayed in 112 segregants of a cross between the yeast strains BY4716 and RM11-1a With 112 samples for these 35 genes, and the eQTLs data, we inferred the regulatory network as shown in There were only 4 instances of reciprocal regulation (two genes act on each other) presented: Among the 145 regulator–target pairs, there are 78 positive regulations, and 67 negative regulations. To verify the inference result, we used the Generate Regulation Matrix tool in the website of YEASTRACT We modeled the gene regulatory networks by using a LR model, and proposed a Bayesian method to complete the inference. We conducted a series of simulations to evaluate the performance of the proposed algorithm LRBI, and compared LRBI with another two algorithms, the AL-Based and the SML algorithms. LRBI had a significantly better performance than AL-based regarding to both PD and FDR. Compared to SML, LRBI showed a better performance in PD and slightly worse in FDR. This feature of LRBI makes more sense. Considering two cases, one is that we can find less false edges but loss more true edges, the other one is that we can find more, or even all true edges among genes, but with slightly more false edges, the latter one is more meaningful.The proposed algorithm was accurate, and the gap between the inferred and the original parameters was less than 5% (even 2%) in most case. The proposed algorithm was also very effective. We inferred the GRN of the 35 yeast genes in a short time (1.2 seconds in a laptop), while for the SML algorithm, a program error occurred after about 52 minutes' run with the same 35 yeast genes data set. LRBI also had the benefit that the dependency of the performance on the estimates of initial parameters is not strong. For simplicity, we just assign some constants to these parameters in simulations and case studies. Therefore, the LR model and the LRBI algorithm can provide an effective way of exploiting both gene expression and perturbation data to infer GRN.The reason our method seemed to perform better was that, LRBI fully exploited the structure of the SEM, and transformed it into a linear regression model without information loss, while AL-Based only partly exploited the structure of the SEM and used the adaptive Lasso to infer the networks, so LRBI was more effective. LRBI used the Bayesian method, while SML essentially used the maximum likelihood method to infer the GRN, therefore SML was not efficient and sensitive to data. However, there are many other methods for linear regression problems, such as hierarchical Bayesian, variational approximation, and so on. These methods can potentially improve the inference accuracy of GRN with the linear regression model proposed by this paper.However, the FDR of LRBI is considerably high when the noise level is large, and another issue is the ability of dealing with large-scale gene networks. Thus, a future work is to decrease FDR in high-noise context, and apply new strategies to handle large-size gene networks.Text S1Bayesian Inference for the Linear Regression Model.(PDF)Click here for additional data file.Software S1Software package implementing the LRBI algorithm.(RAR)Click here for additional data file.


The exposure of bone, tendons, vessels, and nerves in a digital defect is one of the most frequent and severe problems to solve in hand surgery and current approaches are still disappointing. We show the use of an homodigital adipofascial flap taken from the same finger for covering the pulpar defect in a one-step surgical technique able to preserve the digital artery. Restoration of pulpar digital defects with exposure of bone, tendons, vessels, and nerves can be a challenging problem. Several surgical options such as local, reverse flow and free flaps can be performed –4. We prWe report a case of radiodermatitis burn that affects the clamping function of the hand. A wide pulp exposure on the terminal radial neurovascular trunk of the left index occurs as a consequence of surgical debridement .z” incision of the skin overlying the adipofascial flap has been created on the lateral  side of the finger. Consequently, two dermoepidermal flaps were raised. In this way the adipofascial tissue was exposed and the flap was raised from proximal to distal basis. The plane of dissection was superficial to the extensor tendons sheath. During the dissection the flap was harvested very carefully, preventing trauma that might jeopardize its blood supply or devitalize the tissue. When the adequate length of dissection was completed, the tourniquet was released. Meticulous haemostasis was carried out, observing the status of blood circulation at the tip. The adipofascial flap was rotated by 180° in order to place it on the defect, and it was fixed with 5.0 absorbable suture. Over the flap a full thickness skin graft was placed. Postoperatively, the finger was kept immobilized for 72 hours, followed by gradual active mobilization. In this case we obtained the restoration of the clamping function of the hand and after a 6-month follow-up, static two-point discrimination had a mean value of 8 mm.The surgical procedure was carried out under locoregional anesthesia and tourniquet. Firstly, we performed a debridement, which resulted in a great exposition of terminal vessels and nerves. The “This technique showed several advantages. Firstly, the same finger is used to repair the loss of substance without unaesthetic defects. It is a one-stage surgical technique which allows not difficult dissection, avoiding microscopic techniques. No laterodigital arteries (main branches) are sacrificed; the adipofascial flap has a peculiar vascularization based on the dorsal communicating branches of the volar laterodigital arteries. In fact, the volar laterodigital arteries on both radial and ulnar side of the finger provide collateral dorsal communicating branches at the proximal interphalangeal joint which are numerous and constant. According to this observation, we have the chance of interrupting the flow at the ulnar side, rotating the flap on the defect  and maintaining its reliability . Moreove


In Wireless Sensor Networks (WSNs), transmission errors occur frequently due to node failure, battery discharge, contention or interference by objects. Although Directed Diffusion has been considered as a prominent data-centric routing algorithm, it has some weaknesses due to unexpected network errors. In order to address these problems, we proposed a radio-aware routing algorithm to improve the reliability of Directed Diffusion in lossy WSNs. The proposed algorithm is aware of the network status based on the radio information from MAC and PHY layers using a cross-layer design. The cross-layer design can be used to get detailed information about current status of wireless network such as a link quality or transmission errors of communication links. The radio information indicating variant network conditions and link quality was used to determine an alternative route that provides reliable data transmission under lossy WSNs. According to the simulation result, the radio-aware reliable routing algorithm showed better performance in both grid and random topologies with various error rates. The proposed solution suggested the possibility of providing a reliable transmission method for QoS requests in lossy WSNs based on the radio-awareness. The energy and mobility issues will be addressed in the future work. WSNs can be defined as a collection of many tiny sensor nodes forming a self-structured network interconnected by a wireless technology. Since sensor nodes suffer from restricted computing resources and battery life time, the development of effective routing protocols for WSNs have been heavily debated after WSNs were introduced ,2.In the view of limited energy and computing resources, routing protocols for WSNs should be differently designed compared to routing protocols for other wireless networks  which mainly focus on data transmission speed or network throughput ,4. In orThese earlier research studies contributed to diverse domains such as energy efficiency, abstract data access, real time data transmission, etc. Although the diverse approaches have shown their own initiatives in this research area, most studies have the common assumption that routing protocols are run in an ideal network environment without errors. In other words, existing studies did not consider data losses and route failure of the wireless networks due to unpredictable errors such as node failure, contention, interference or fading ,14.Directed Diffusion (DD)  is a proRecently, a concept of cross-layer design has emerged to utilize radio information which reflects the current status of the wireless network. The radio information is considered as an important design factor to provide reliable data transmission services with WSNs ,16. The ns-2 under various conditions. In accordance with the simulation results, the proposed algorithm showed better performance in various environments with different error rates.In this paper, we proposed a Radio-Aware (RA) routing algorithm which can improve the reliability of DD in lossy WSNs. The proposed algorithm can provide an optimum route to react to lossy WSNs based on the radio information which reflects the current network status by using a cross-layer design. Furthermore, if RA detects the lossy situation at a target node, it can choose an alternative neighbor node as a next hop to provide reliable data transmission. Since we added the cross-layer design that utilizes radio information of existing packets, RA has the advantages of reliable communication and interoperability in lossy WSNs without burdening additional overhead on DD. In order to evaluate RA, several simulations were performed with The remainder of the paper is organized as follows. In Section 2, we discuss related works and motivation. Section 3 describes the proposed radio-aware routing algorithm. In Section 4, we explain the evaluation metrics of the simulation. The simulation results are analyzed in Section 5. Section 6 concludes the paper and addresses future work.2.2.1.DD  is a datIn the first period, a sink and sensor nodes collect the routing information by propagating an interest for transmitting their data. When a sensor application requests particular sensing data, a sink creates an interest specifying the target data and then disseminates the interest over WSNs. The interest represents the application's request including attribute-value pairs that indicate target sensing data. When the interest arrives at each node, the node checks whether the request matches its own sensing data or not. At the same time, the node sets up a gradient toward the direction to the sink node and forwards the interest to its neighbors. After setting up gradients, the route can be determined at each node with its value of gradient using the routing algorithm of DD.The second period is started after interests are propagated across the network. In the second period, sensors are triggered to start sending the data and a sensor application begins collecting data. The target sensing data are aggregated to a sink based on the route with the value of gradients. There are several versions of DD. The original, Two Phase Pull (TPP) offers efficient communication between sources and sinks. TPP consists of two phases of interactions which can provide data collection service using several control packets: interest, exploratory data, and positive/negative reinforcement.The first phase of TPP is composed of the interest flooding and exploratory data to find source nodes and to set up gradient at all nodes. The initial data from the source node is marked as exploratory data and is sent again to all neighbors which has matching gradients. The second phase of TPP is a step to reinforce the route and to send data through the reinforced route. The positive and negative reinforcement packets are used to increase or decrease the gradient on a certain route. Especially, the negative reinforcement suppresses loops or duplicated routes that may arise due to the network changes. After the route was reinforced, sensing data is sent through the reinforced route to a sink.The control packets of TPP for recovering network changes enable TPP to provide robust communication in WSNs. However, since too many interest and exploratory packets flooded to the networks, the energy of each sensor node tended to be depleted rapidly in TPP.In order to overcome the problems of TPP, One Phase Pull (OPP) was proposed to eliminate explicit exploratory data and reinforcement packets. OPP has only one phase to provide collecting sensing data using an interest packet only compared to TPP. OPP used the interest only to set up the gradient and sent the data directly on the selected route without additional control packets. This simple feature of OPP is a significant benefit because it can minimize the flooding of TPP. When an interest arrives at a source node, it does not mark its first data as exploratory data. The source node sends data only on the route with preferred gradient. The preferred gradient is determined by the neighbor who was the first to send the matching interest. Therefore, in OPP, the lowest latency route is implicitly reinforced without requiring additional reinforcement packets.OPP contributed to saving energy at each node which results in extending the network life time without requiring additional control packets of exploratory data and reinforcement packets. However, the communication route between sources and a sink is assumed to be asymmetric due to the different direction when the route is determined during interest propagation period. The novel method should be considered to overcome the link asymmetry problem at the protocol level.2.2.Although DD was proven to have several advantages such as providing a robust, scalable and energy efficient routing scheme, it has several limitations. Firstly, the protocol overhead should be reduced to minimize flooding of control packets. Although the control packets are essential to set up a route from route failure or data loss, increasing flooding packets causes excessive energy depletion of sensor nodes. Secondly, other considerable metrics for route decision had to be applied to support various QoS such as data rate, energy, reliability, or latency. However, there is little research to change the gradient to other metrics as well as DD. That is, the data rate is still the only metric used to set up gradients in DD.Thirdly, DD cannot deal with lossy WSNs resulted from the unstable network, because a single session of DD is separated into interest propagation and data aggregation periods. If the lossy situation occurs in the data aggregation period, a node cannot send sensing data along the pre-decided route because DD cannot make a new decision to recover the lossy situation on the route in the data propagation period promptly.Finally, link asymmetry may be a considerable issue in wireless networks. A supplement is needed to remedy the asymmetry of a routing protocol, because the data path between nodes and a sink is determined by interest propagation from a sink to sensor nodes in DD. A routing protocol for lossy WSNs has to address the asymmetry characteristics to provide link symmetry.In order to resolve these issues of DD, two kinds of approaches has been introduced. Firstly, some studies focus on prolonging the network life time using energy awareness. In ,20, the Generally, real WSNs should be assumed as unstable due to the nature of wireless communication. The instability of WSNs can be defined as lossy WSNs including many reasons: contention, interference, node mobility, fading by object, or node failure. Lossy WSNs are our error model to emulate real sensor networks having those unpredictable errors. The unpredictable data losses and route failures may occur in lossy WSNs. This random failure can be also defined as the lossy situation in our assumption.The earlier studies tried to overcome the problems of DD and contributed to extending network lifetimes by considering energy, load balancing, or QoS. However, the previous studies cannot provide the reliable data transmission in lossy WSNs, because they assumed that the network is stable. Furthermore, the previous studies are lack of methods to complement link asymmetry of the wireless networks. In the previous studies, they carry over the supplement of link symmetry to the detecting and filtering function of MAC layer .When the network topology becomes unstable, the lossy situation of DD will increase and DD needs to flood more control packets across the network to adjust the error conditions. Essentially, since DD is separated into the route setup (interest propagation period) and data transmission (data aggregation period), DD cannot send sensing data along existing routes when the WSNs become unstable. The more data loss control packets are flooded, the lossier situation occurs due to the rerouting, retransmission, and route failure.In order to overcome the lossy situation, a routing algorithm is required to consider up-to-date network conditions. If the routing algorithm can recognize the current status of WSNs, the lossy situation can be adjusted to provide a reliable data collecting service to sensor applications under lossy WSNs.3.The proposed algorithm (RA) is different from previous works that try to improve DD by using the additional control session. The novelty of the proposed routing algorithm is that RA is designed to remove the burden of additional control session. By adopting cross-layer design instead of the additional control session, RA can deal with the lossy situation without additional overhead.The previous algorithms need an additional control session to deal with lossy situation within a transport layer which controls the error recovery process such as the retransmission of control packets, rerouting, and error correction process. The additional control session can recover the lossy situation, but it imposes an additional overhead and complexity on the original features. Furthermore, their modification of DD without consideration of the radio information may give disadvantages to break originality and to burden overhead and complexity.DD already has the traditional control session at the application layer  and thisFurthermore, as the authors of  pointed On the other hand, RA can provide the method to utilize the radio information across the layers between the MAC and the network layers using cross-layer design. RA updates the radio information into the neighbor table. The neighbor table is shared between MAC and network layer to decide a route with best gradient. When the network becomes unstable in lossy WSNs, RA also suffers from the lossy situation. However, RA is able to restore the route in according to the variant network status. RA can provide reliable data collecting service without additional overhead and complexity for optimizing the route in lossy WSNs.3.1.Several MAC protocols  can be candidates for WSNs, because the MAC protocols of WSNs should minimize energy consumption. However, as various sensor applications are developed , WSNs have to deal with new requirements such as scalability, throughput, QoS , and interoperability with existing infrastructure. Although a specific sensor MAC gives several advantages of saving more energy on its operation, the restricted energy efficient MAC cannot support all of the various requirements.In , they shThe author of DD also used a modified 802.11 MAC to emulate realistic sensor network radio more closely. They altered the ns-2 radio model to fit a sensor radio specification minimizing energy consumption. The energy efficiency of 802.11 MAC has to be considered, but we believe the effect of the various utilizations of 802.11 MAC to give more advantages to WSNs.3.2.In this paper, we proposed a radio-aware (RA) routing algorithm to address lossy situation. RA not only maintains the advantages of DD, but also applies radio information to set up the route with cross-layer designs. In order to address the problem mentioned above, RA is designed to improve the reliability of DD in lossy WSNs. Firstly, RA can exchange routing information among nodes independent of the period. In RA, the route will be adjusted to the situation caused by unexpected errors in lossy WSNs.Secondly, RA introduces radio information which represents the air time cost and route failure as new metrics of gradient. Since these new metrics can reflect current status of networks, RA can maintain consistent connectivity and achieve reliability in lossy WSNs. In addition to the metrics, the position of each sensor node is used to decide the closest direction from source nodes to a sink.Finally, the fast re-route scheme is adopted to address the lossy situation in addition to the link asymmetry. In the fast re-route scheme, the lossy situation can be fetched through the RTS/CTS mechanism of the CSMA/CA at the 802.11b MAC. The route failure flag indicates the lossy situation of the error-prone node. RA can choose an alternative node instead of a failed node. Moreover, the fast re-route scheme gives reduction of link asymmetry problem of wireless network.In RA, the air time cost and the position of each sensor node were collected to set up gradients whenever interest and data packets were received on each node. RA can calculate the air time cost, and the position of each node is included in the interest. If a source node has a problem in sending data due to route failure, RA can select an alternative route instead of failed one by utilizing fast re-reroute scheme.3.2.1.aC) represents the cost for occupying the channel when transmitting a single frame on the wireless channel. A route with the smaller air time cost can be considered as a better route than others, the route can be selected with higher priority for data routing. The air time cost can be measured from Var.DescriptionOOverhead (699 μs)BtNumber of bits in test frame rBit rate in MbpsfeFrame error rateThe air time cost (aC) was used in an 802.11b wireless mesh network [O is the sum of caO and p. OcaO is the channel access overhead and pO is the protocol overhead. tB is the number of bits in the test frame. Some representative values for these constants are: caO = 335 μs, pO = 364 μs, and tB = 8,192 bits in 802.11b network. The input parameters of r and fe are the bit rate in Mbps and the frame error rate for the test frame size tB, respectively.From  network  and it w3.2.2.The radio-aware routing algorithm consists of three parts: gathering the radio information, route selection, and a fast-reroute scheme. In the first step, the radio information for the wireless network was collected and updated through the cross-layer design  at the MWhen a node receives an interest packet from its neighbor, the node puts all the values of the neighbor node into the table, except the position of a sink. In our assumption, all the nodes are aware of their own position and the sink node's position when the nodes are deployed. However, since a node cannot know the neighbors' position, they have to exchange their position data. In order to exchange the position data, we added the data field to the interest packet. When an interest packet was propagated to neighbor nodes, each node put their position data into the interest instead of previous forwarding node's position data. If a node receives the interest from the neighbors, the node updates its neighbor table with the information of previous node.The radio information is updated whenever a node receives both interest and data packets from neighbors to indicate the air time cost and a route failure flag. The air time cost can be calculated from The complete neighbor table was composed of a node ID, the position  of a node, the air time cost, and route failure flag as shown in 3.2.3.RA is originally designed to select an optimum route based on the distance and the air time cost. These two metrics of RA have an important role to make a route decision respectively. If a routing protocol decides a route based on the distance, the protocol can choose the shortest route between a source and a sink. However, the routing protocol only based on the distance information cannot properly cope with the lossy situation because the distance information does not have the information of the current network's status.Whereas, if a routing protocol uses the air time cost for deciding the route, the protocol can choose the best route based on the current network's status. However, if a routing protocol only considers the air time cost, the direction of the data transmission can be opposite to the data forwarding route from a source to sink. The effect of inverse directed data transmission causes the long route or loop on the communicating route. For these reasons, we designed RA to take the advantages of each scheme for choosing optimum route based on two metrics to overcome lossy situation. The detailed operation of RA is as follows.ref) of the sender S. The distance to the sink node of each node was considered to avoid the loop and longer hops caused by selecting an irrelevant node with inverse direction.The air time cost was calculated and updated whenever receiving the interest and data from its neighbors. Also, the distance between each node and its sink node was calculated by examining the interest packet including the position of the sink node. Each node already knows the position of sink node and neighbor nodes from interest propagation. This directional information can be used to find closer nodes to the sink among neighbor nodes by comparing with the reference distance  can be calculated from the The node selection process of the proposed algorithm is as follows. When a sender S receives the packets of both interest and data from the neighboring nodes, it calculates the air time cost for each node and updates it into its neighbor table from 1, N2, N3 are selected as candidate nodes. Among them, N1 will be selected as the next node because it has the smallest value of the air time cost. Although the node N4 has the best air time cost (0.7615) among the neighbors, N4 will not be selected as the candidate because the direction to N4 is inverse to the sink node from the sender S.1 instead of N4 with the best air time cost is as follows. Firstly, in order to prevent a sender selecting long route or loop, RA chooses candidate nodes with shorter distance than the reference distance of the sender. Secondly, RA chooses an optimum target node among candidate nodes with highest link quality which can reflect on the current network's status.The reason for selecting N3.2.4.In the proposed algorithm, the route was determined by the air time cost, but lossy situation still occurs when a next target node fails. At the moment, even the proposed algorithm cannot send data to the failed node without the fast re-route scheme, because the sender S has not updated the route failure information if the node error occurs at unspecified time. This route failure problem can be promptly fetched by using the fast re-route scheme. When a node detects the route failure, the route failure flag was set to 1. Then, the fast re-route scheme promptly starts to select alternative route that will replace the failed route.The route failure flag is set by the RTS/CTS mechanism of the 802.11b MAC CSMA/CA protocol . The RTSIn this case, the route failure flag is included to decide an alternative node in addition to the air time cost and direction. The failure node, N was removed from the list of candidate nodes, and S chooses another node which has the best air time cost among remained candidate nodes in the table. After S selects a new target node, S starts to run the fast re-route scheme repeatedly until the data transmission succeeds.3.2.5.RA showed better performance than DD in lossy WSNs, but link asymmetry still existed in RA. The link asymmetry is a well known problem which may affect variation of upper layer in wireless networks . EspeciaIn  the authHowever, DD just delegates a solution for addressing the link asymmetry to MAC layer. In contrast, RA adopted two available solutions among the proposed methods to overcome the asymmetric problem at different layer. A multi-round discovery was operated at network layer and the learning function was run at MAC layer. Updating the air time cost is a multi-round discovery technique at network layer in RA, whenever a node receives both the interest and data packet. This method reduces high overhead of DD due to the multiple round of flooding.In addition to the multi-round update of the air time cost, RA adopted a fast re-route scheme to guarantee the symmetry communication at MAC layer. If a node had lossy situation on a certain link, the fast re-route scheme was able to detect the link asymmetry. After the fast re-route scheme updated such asymmetric information on that link, the failed link was deleted from the neighbor table for an alternative route decision.As a result, RA is able to guarantee a symmetric link to transmit data across the selected route using the multi-round update of the air time cost and the fast re-route scheme as an active method. RA can also provide optimum selection of a route without additional overhead and complexity. When the WSNs became unstable, these additional features of RA showed more improvement than DD.4.4.1.Generally, the route of DD is established along the shortest path between source and sink. As the time passes, the energy of a node can be drained very quickly on the shortest path. The rapid dissipation of a node's energy brings node failure resulting in lossy situation. Consequently, a routing algorithm extends the shortest path including adjacent neighbor nodes instead of failed nodes to keep the connectivity of WSNs. We defined this extended path including the shortest path as a Main Communication Path (MCP). The MCP is an extended path constructed by a group of nodes enclosing the shortest path and adjacent neighbors of the path.DD is designed to transmit most data over the MCP consisting of the shortest distances between nodes. As referred from ,23, the For example, in the case of one source and one sink at each corner (left-bottom and right-top), the shortest path extended to adjacent neighbors forming a diagonal shape.For example, the error group on the MCP is composed of 56 nodes for the grid topology and 112 nodes for the random topology. If the error rate is 5%, five nodes are selected as error-prone nodes from the nodes located on MCP at every selection. That is, in each selection, error nodes will be randomly selected from the sensor group on the MCP according to given error rate.To model lossy WSNs, we make the randomly selected nodes to be blinked for a random time. Blinking selected nodes emulate the error-prone nodes to generate lossy situation as a random failure. The random failure of the selected error nodes is to avoid influence on interest propagation period for setting up gradient in DD. The interest propagation period is important to recover the lossy situation in DD. However, the lossy situation may occur in lossy WSNs independent with the interest propagation period as random failure. The author of DD mentioned the interest propagation period is a protocol design factor to trade-off between overhead and robustness.The interest propagation period on time of original DD is a sum of fixed interval (30 seconds) and random time with large variance (range: a few ∼ hundreds seconds). However, this randomness might affect the number of resulted packets and the small interest interval of DD made a lot of interests flooding which might be a burden to analyze the effect of lossy WSNs.Since the main purpose of lossy WSNs model is to generate unpredictable errors independent with the interest propagation period, we eliminates the randomness of DD to avoid difficulties in analyzing the result. Instead, in order to make the unexpected lossy situation, the fixed interval (60 seconds) is used for interest propagation and randomly selected nodes blinked for random time on MCP during the simulation.4.2.ns-2 simulator [For the performance analysis of the reliability in lossy WSNs, we measured the data deliver rate, the protocol overhead, and the average transmission delay time during simulations with imulator . We perfIn the first simulation, 100 sensor nodes are located in the 200 m × 200 m network topology for the grid topology. 200 sensor nodes are deployed in the same network size as a uniform random distribution for a random topology. A sink and a source are placed at each corner during the first simulation.For the analysis with multi sources and one sink, the network topology is the same as the grid topology, but the number of sources and a sink position is different. The shape of MCP depends on the position of sources and sinks. There are three different types of MCP in multi source experiments.ns-2. We followed the same radio model and energy model used in DD. The transmission range of a node was only changed to 30 m to maintain the macroscopic connectivity of a sensor field. Other parameters were not modified to exclude any influence on the original DD. The detailed simulation parameters are shown in DD implemented the 802.11b MAC model to emulate realistic sensor radio  in the n4.3.The authors of  summarizWe measured the data delivery rate, the protocol overhead, and the average transmission delay to compare One Phase Pull (OPP) with the proposed algorithm (RA) in lossy WSN model with various topologies:deliveryR) is the percentage of successfully delivered packets at a sink, where sourceN is the number of packets sent from the source node and sinkN is the number of successfully received data at a sink node in Data delivery rate (protocolO) is a ratio between the number control packet and data packet, where cotrolN is the number of control packets and dataN is the number of data packets in protocolO indicates that many control packets is needed to deliver data to a sink successfully.Protocol overhead (avgT) represents the average time difference on successfully received data at a sink from the source node, where sourceT is the sending time of data at source and sinkT is the receiving time of the same data at a sink and deliveryN is the number of delivered data in Average transmission delay  and the proposed RA were compared in a grid topology with error rates of 0%, 5%, 10%, 15%, and 20%. The protocol overhead of RA is also smaller than OPP. The protocol overhead of OPP increased more as the number of control packets and transmission failures increased. RA maintains low protocol overhead compared to OPP, RA does not affect the error rate significantly in spite of increasing the error rate with RA. RA keeps a higher data delivery rate at every error rate. In addition, the air time cost and the fast re-route scheme are used to provide reliable communication with RA. Additional control packets do not need to be sent to remove the loop. RA re-routes to an alternative path after a failure. For this reason, although the total number of packets with RA is larger than that with OPP, the data delivery rate and the protocol overhead of RA are better than OPP. The average transmission delay of RA is quite smaller than that of OPP for all error rates. As the error rate increases in lossy WSNs, OPP suffers from a data loss. In this case, OPP cannot send data to a sink unless the route is set up again by interest propagation. When data transmission fails, the maximum delay time follows the interest propagation cycle. In this paper, we used 60 seconds as the interest propagation cycle. Since RA is able to re-route using the air time cost and route failure information during data transmission period, RA shows a small average transmission delay. However, if the error rate is above 15%, the alternative routes decrease, and the average transmission delay increases slightly. The results are shown in 5.2.In the second simulation, the performance metrics of the original DD (OPP) and the proposed RA were compared in a random topology with diverse error rate at 0%, 5%, 10%, 15%, and 20%.The protocol overhead of both OPP and RA with random topology is four times less than that with the grid topology. Since the number of nodes increases and the distance among neighbor nodes gets random, the number of control packets also increases. The number of data packets in the network decreases with increasing error rate, as shown in In the random topology, the total number of packets was increased to three times more than that with the grid topology. As the total number of packets increases and the data delivery rate decreases, the protocol overhead increases consequently. RA shows a higher total number of packets than OPP in In the random topology, less data is successfully sent to the sink node compared to grid topology. The average transmission delay occurs, because the distance between neighbor nodes is different from a random deployment of nodes. This random distance tends to affect the gradient set up for each route when an error occurs at a node. As a result, OPP shows a higher average transmission delay at every error rate compared to OPP in the grid topology.RA has a smaller average transmission delay for data transmission than OPP. However, compared to the grid topology, the random distance affects the setup of the route in RA as well. Although the distance was considered using RA algorithm, this case is slightly different to the effect of random distance. If a target node is out of range from the sender, the node cannot be a candidate node in the sender's alternate route. In this algorithm, candidate nodes within the communication range of the sender node are only considered as alternate routes, accounting for the low performance of random topology compared to grid topology.5.3.This section describes the effects with different position of multiple sources and a single sink for further analysis. The error group may vary according to the position of sources and a sink. The different number of sources and the position of a sink may slightly degrade the advantages of proposed RA due to the different shape of error group in MCP.In this experiment, we performed three simulations with different MCP including multiple sources and single sink. We defined three topology based on the shape of each MCP to identify topologies: arrow type, cross type, and diamond type. However, when the network is stable (error rate: 0%), all of OPP show better data delivery rates  than the case of RA (around 70%). The performance degradation of RA is resulted from interferences increased around a sink. When data arrives at the neighbors of a sink, a neighbor has to choose a next hop to send data to a sink. At the same time, if another neighbor tries to choose a route as the same way, the re-route scheme of the node is executed to avoid lossy situation. The packets generated by the re-route scheme may be additional overhead. For this reason, if a sink has insufficient candidate nodes, the interference may increase more in RA.The comparison of total number of packet is as shown in The average transmission delay of the cross type is worst than others as well as other metrics, because the four sources may use different path. Compared with other type, a cross type intermediate node has a different path that creates additional overhead from maintaining multiple paths for each source node. In other types of topology, the path from a source to a sink can be shared by an intermediate node. This behavior of the cross type to maintain multiple paths can be a reason for degrading performance.We verified that RA shows even better performance than DD with different position of multiple sources and one sink. DD suffers from lossy situation a lot more than RA in this environment. DD remains the problem which cannot provide reliable and efficient communication in lossy WSNs. However, RA shows performance improvement regardless of topology, different position and multiple sources. The performance degradation of RA can be seen as little at the experiment with different MCP, but the performance fluctuation according to the error rate is less than the first experiment with one source and one sink.6.WSNs have unique characteristics such as resource limitations, communication flow from many to one, and deployment in error prone environments. Many routing protocols have been developed to address these characteristics. These routing protocols mainly contributed to prolonging network survivality by saving energy during communication, but most research studies do not provide reliable communication in an error-prone environment. Lossy WSNs results from contention and interference among nodes, node mobility, fading by object, or node failure from battery discharge and physical damage.We proposed a radio-aware routing algorithm to improve the reliability of DD in lossy WSNs. DD is well-suited for WSNs' routing because it uses local interactions among distributed nodes. DD also provides data-centric, robust, and scalable models, but previous research did not consider the unstable network. The data delivery rate of DD decreased very quickly in an error prone environment.In this paper, to handle the lossy environment of WSNs properly, RA algorithm which uses radio information was proposed. RA algorithm is a data centric routing protocol like the original DD, but RA algorithm can be aware of the network's status by adopting a cross-layer approach between MAC and network layers. Furthermore, a fast re-route scheme was also applied to provide consistent and quick re-transmission for reliability. The radio-aware reliable routing algorithm contributed to providing QoS, reliability, and reduction of DD's overheads for lossy WSNs including scalability and interoperability.ns-2 under various conditions. Lossy WSNs were emulated by randomly blinking sensor nodes to create random node failures. According to the simulation results, the radio-aware routing algorithm showed better performance for each error rate. The error model for five simulations contained grid and random topologies with diverse MCP including multiple sources and different sink position at error rates of 0%, 5%, 10%, 15%, and 20%. As the error rate increases, the proposed solution showed higher data delivery rate, lower protocol overhead, and lower average transmission delay as compared to DD regardless of the error rates and topologies.To prove the effectiveness of the proposed algorithm, the simulation was performed with The simulation results demonstrate the possibility of providing a reliable transmission method for QoS requests in lossy WSNs by using radio-awareness. However, when a node chooses its forwarding node based on our algorithm, the re-route scheme consumes more energy due to packet retransmission. The energy consideration and mobility will be addressed in future work.


In the following review we will discuss the basic aspects of fibrosis assessment with MR and its utility for diagnostic and prognostic evaluation. We will also address the topic of cardiovascular inflammation imaging with 19F as a potential new development that may broaden the indications for MR in the future.Late gadolinium enhancement is the technique of choice for detecting myocardial fibrosis. Although this technique is used in a wide range of cardiovascular pathologies, ischemic cardiomyopathy and the workup for myocarditis and other cardiomyopathies make up a significant proportion of the total indications. Multiple studies during the last decade have demonstrated its utility to adequately characterize myocardial tissue and offer diagnostic and prognostic information. Recent T1 mapping techniques aim to overcome the limitations of late gadolinium enhancement to assess diffuse fibrosis.  Late gadolinium-enhanced (LGE) magnetic resonance (MR) is the technique of choice for assessing fibrosis, as demonstrated by the vast amount of evidence arising from both animal and clinical studies , 2. Its The basic principle of late gadolinium enhancement imaging lies in the delayed washin and washout of gadolinium contrast media in tissue with an increased proportion of extracellular space. It is important to note that since LGE is based on differences in extracellular space in different areas of the myocardium, it is only useful if the disease is localized . While that is usually not a problem in ischemic heart disease, it may be an issue in other pathologies in which general fibrosis plays an important role .Although the indications for performing an MR exam with LGE cover multiple cardiovascular pathologies, a recent European survey  has poin19F) which, although currently mainly restricted to research purposes, may be used in the future in clinical practice.In this review article we discuss the basic aspects of fibrosis assessment with late gadolinium enhancement as well as its main applications in the field of ischemic heart disease, myocarditis, and other cardiomyopathies. Finally, we offer an insight into new promising techniques  is an ion with paramagnetic properties, which is toxic in its unbound state. Therefore, the contrast agents (CAs) consist of a large molecule which comprises a Gd ion and a carrier agent that keeps Gd bound during intravenous injection, while still retaining its imaging properties. Gadolinium CAs are relatively large in size which prevents them entering the intracellular space under normal conditions and are thus extracellular agents. Some CAs have a particularly large molecule , remain mainly intravascular with just a small degree of extravasation, and are considered vascular agents . Most of the CAs are renally excreted, have a half-life of 2 hours, and are almost completely cleared from the bloodstream after 24 hours. 2) or end-stage renal failure, which can be acute or chronic, and who received repeated high doses of Gd CA in a short period of time. Although liver transplantation was also considered to be a risk factor for the development of NSF, some reports seem to suggest otherwise [Adverse reactions to Gd-based CA are uncommon, although potentially severe. In addition to allergic reactions , a potential adverse effect of Gd CA is nephrogenic systemic sclerosis (NSF), a rare fibrosing disease involving the skin, subcutaneous tissue, and internal organs that can cause severe disability and even death. The reported cases of this disease involve patients with severe . The reasons for this increase in the distribution volume vary according to the underlying pathology: in the case of acute myocardial infarction, it is caused by myocardial edema and myocyte necrosis and lysis , while iGd reduces T1 time, which is a tissue-specific parameter. Since every tissue has its own T1 value, this parameter can be used to differentiate between tissues. The MR sequences in which most of the contrast between tissues is due to differences in T1 values are called T1 weighted and are the ones used to obtain images in the LGE technique. In particular, the inversion-recovery gradient-echo sequence used nowadays provides an excellent contrast between normal myocardium and scarring by nulling the signal from normal myocardium  .The LGE technique consists of an injection of a 0.1-0.2 mmol/kg bolus of Gd-based CA, followed by T1-weighted acquisition 10–20 minutes after administration of the contrast. The main determinant factor for obtaining good quality images is the correct selection of an adequate inversion time (TI). This parameter is highly variable among patients and physiological conditions, and its correct selection is essential to achieve an effective suppression of normal myocardium . The TI is selected either visually or by using a Look-Locker sequence, which offers a series of images with variable TI and allows the examiner to pick the one with the best contrast. Phase-sensitive inversion recovery (PSIR) techniques are an alternative to this approach.Hyperenhancement on LGE has a close correlation to histopathologically proven myocardial necrosis, as demonstrated by some studies. In an experimental study, Kim and colleagues  demonstrSome initial animal studies suggested the potential utility of LGE MR in acute myocardial infarction (MI). In a canine model of MI, an increase in the partition coefficient of Gd in the infarcted regions was observed, which was significantly different from noninfarcted regions. This increase in the partition coefficient started 1 min after reperfusion (following 2 hours of coronary occlusion) and persisted until 8 weeks , 13. LGEIn addition to the detection of the infarcted area, MR permits the detection of the ischemic area at risk by combining LGE MR with T2-weighted black blood imaging. Although this area does not demonstrate enhancement, it typically demonstrates a high intensity in T2-weighted imaging. In addition to its diagnostic utility, the extent of the enhanced myocardium seems to be related to functional parameters at followup. Acute MI mass was demonstrated to be correlated not only with peak troponine I but also with left ventricle (LV) ejection fraction (EF) at followup . FurtherAnother important determinant of prognosis in patients with acute MI is the presence of a microvascular obstruction (MVO), which can also be successfully visualized by MR as an subendocardial area of low signal intensity inside the enhanced region. The assessment of MVO can be performed during the contrast first pass, early (2–5 min) after its administration, or on late gadolinium-enhanced images. It has to be taken into account that the size of MVO decreases over time , but a lower variability on quantification has been obtained with LGE imaging . The preLately, there has been a growing interest in the presence of infarct hemorrhage as a strong marker of adverse cardiovascular events and LV remodelling . AlthougFinally, the infarct peripheral zone has also been shown to provide prognostic information and can also be detected by LGE MR. In the peripheral zone both viable and nonviable myocytes are present and constitute an arrhythmogenic substrate, whereby the extent of the peripheral zone is a predictor of cardiovascular and all-cause mortality . The perAlthough both acute and chronic MI scars show LGE, the presence of wall thinning and the absence of hyperintensity in T2-weighted imaging suggest chronic MI . In addiSimilar to acute MI, in chronic ischemic heart disease (IHD) MR can be useful to predict the probability of functional recovery with an inverse relationship between the transmural extent of LGE and the likelihood of functional improvement after revascularization . The perOn the other hand, the concomitant use of low-dose dobutamine stress MR may offer additional information to that of LGE MR alone, both in terms of functional recovery  and to pIn Takayasu arteritis, which usually affects the aorta and its main branches, the presence of delayed enhancement of the artery wall seems to be correlated with inflammatory markers and disease activity , 32.Detection of LGE in the coronary artery wall is also feasible. In a study by our working group, the presence of LGE in the coronary artery wall of all coronary artery disease (CAD) patients could be observed, but it was not seen in any of the healthy subjects . In addiHowever, the exact significance of the presence of enhancement on the coronary wall remains unclear, as demonstrated by the fact that LGE of the coronary artery wall has also been found in patients with Takayasu arteritis, with a similar proportion to that of CAD patients . The LGEFinally, the potential utility of Gd-enhanced MR for differentiating between atherosclerotic coronary plaque types has also been demonstrated . In addiThe assessment of LGE in the coronary wall currently remains limited to research studies and is not yet used in daily clinical practice. MR is a useful technique for the diagnosis of myocarditis. The typical pattern of LGE in myocarditis is the presence of patchy enhancement with a subepicardial localization, usually affecting the inferolateral segments and, less frequently, the anteroseptal region  Figure . The hyperenhanced areas correlate also with histologic active inflammation. In a study with both MR and biopsy, histologically active myocarditis was found in 19 out of 21 patients with biopsy coincident with the area of LGE, but in only one patient in which the biopsy region did not coincide . In addiLGE MR can also offer prognostic information in patients with myocarditis. In a recent work including more than 200 patients with biopsy-proven myocarditis and MR, LGE was the best independent predictor of both all-cause and cardiac mortality, with a hazard ratio superior to that of functional class, EF, or LV volumes .The LGE method is also useful for the evaluation of other nonischemic cardiomyopathies. Although different patterns of LGE have been described for various nonischemic pathologies, the key characteristic is that, in contrast to ischemic cardiomyopathy, the subendocardial region is usually not affected. The hyperenhancement regions are usually subepicardial or midventricular and do not correspond to any known coronary territory. In the case of amyloid, the main distinct feature is the difficulty of choosing an optimal TI value to null the myocardium .In addition to its diagnostic utility, the presence of LGE can also offer prognostic information in some entities. In the case of hypertrophic cardiomyopathy, the presence of hyperenhancement has been shown to be an independent predictor of both cardiovascular and global mortality, and of ventricular arrhythmias , 46.As it has been described before, although LGE reliably detects localized fibrosis, it is limited in the assessment of diffuse fibrosis. The main reason for this limitation lies in the fact that the LGE technique detects fibrosis on the basis of the difference in signal intensity between an enhanced area and the normal myocardium. As a consequence, no signal intensity difference will be detected if there is a pathology which causes diffuse fibrosis. In order to overcome this limitation of LGE, T1-mapping techniques have been developed which permit the assessment of diffuse fibrosis. This new method allows for evaluation of pathologies which, although associated with a high fibrosis content, cannot be studied with LGE. These T1-mapping techniques permit the direct quantification of the myocardial T1 time. In the setting of an increase in collagen fibers (and thus extracellular space), T1 time decreases after contrast administration. In a recent study with cardiomyopathy patients with endomyocardial biopsy, postcontrast T1 times correlated histologically with fibrosis  and wereThe modified Look-Locker inversion recovery (MOLLI) sequence is frequently used to measure T1 times, and its reproducibility has been demonstrated . MOLLI TDespite promising results, T1 mapping is currently still limited to research purposes and is not yet widely used in clinical practice.19F to detect inflammation. Because fluorine is only found in the body at very low levels, there is no background signal and it offers excellent specificity [19F, have the advantage of being taken up by the reticuloendothelial cells and, as a consequence, can be used to track these cells. Once they are administered intravenously, PFCs are taken up predominantly by monocytes [This technique is based on the utilization of cificity . Perfluoonocytes  and tranIn a study with a murine model of MI, the authors demonstrated the feasibility of visualizing with fluorine MR a infiltration of PFCs in the border zone of the infarcted areas, which corresponded to cells of the monocyte/macrophage system in the histologic analysis . Another19F MRI is a promising method for imaging cardiovascular inflammation although it is currently still limited to research purposes. The further development of these promising new techniques will expand the application of MR in the future.Late gadolinium-enhanced MR is an essential technique for assessing cardiovascular localized fibrosis. In the field of myocarditis and ischemic cardiomyopathy, LGE MRI is a valuable tool that offers diagnostic and prognostic information and can guide therapeutic decision. Although still not widely available in clinical practice, T1 mapping offers the possibility of overcoming the limitations of late gadolinium enhancement by visualizing diffuse fibrosis. Finally,


Prompt and accurate diagnosis of acute ischemic stroke is critical to seek acute therapy. In traditional Chinese medicine (TCM) science, there is a comprehensive system of diagnosis and medical care of acute ischemic stroke. Here we introduce a two-level model for the analysis of TCM syndrome of acute ischemic stroke. Owing to the limitation of sample size and imbalance, we focused on the analysis of wind-phlegm collateral obstruction syndrome (Feng Tan Yu Zu Zheng). Firstly, a Support-Vector-Machine- (SVM-) based diagnostic model was set up through selection of core symptoms. After pairwise undersampling, we improved the performance of prediction and generated the core symptoms-based diagnostic model of wind-phlegm collateral obstruction syndrome. Next, Pathway Pattern-based method and MetaDrug platform were used to shed light on the molecular basis of the significance of core symptoms in three complementary aspects: symptom-gene-pathway multilayer correlation network, enriched pathways, and most relevant interaction network. The integration of diagnostic model and molecular mechanism analysis creates an interesting perspective for better understanding the syndrome. The two-level model would provide a new opportunity for the study of TCM syndromes. Acute ischemic stroke occurs as a result of a disturbance in the blood vessels supplying blood to the brain. In spite of improvement in health care and medical condition, the prevalence of stroke has been increasing in aging society . It is wHowever, many acute ischemic stroke victims fail to receive medical treatment in time. In fact, stroke treatments are time sensitive . Delay iMeanwhile, many acute ischemic stroke patients would choose traditional Chinese treatments as an alternative medical practice in China. In fact, practitioners of TCM have long focused on the medical care of stroke –16. And  As assessing all of the recorded symptoms relevant to acute ischemic stroke in TCM science is time consuming and impractical clinically, it is pivotal to select core symptoms for the accurate diagnosis of syndromes of acute ischemic stroke. Some models have been proposed to generate diagnosis criteria for syndromes of stroke. Jiang et al.  develope With the development of systems biology, it has been possible to integrate phenotype data, omics data, chemical data, pathway maps, and interactions of heterogeneous data in the study of molecular mechanism of core symptoms of TCM syndrome. In this work, we propose a two-level model  for the We enrolled acute ischemic stroke patients within 72 hours after their ictus from several hospitals located in Northern China from August 2007 to December 2009. All the patients are aged between 35 and 75 without some specific predefined diseases. The diagnoses of syndromes were made by experienced doctors of TCM on the basis of . A questWe choose SVM to generate the diagnostic model, which is essentially a classification schema, because SVM is based on strong theoretical foundations and performs well in many application domains . As the As illustrated in C, we define entropy to measure the uncertainty of C asC after observing values of another variable X is defined asP(ci) is the prior probabilities for all values of C, and P(ci | xj) is the posterior probabilities of C given the values of X. Then the information gain for C and X is given later to reflect the additional information about C provided by X:C and symptom as X. Thus the information gain between each symptom and the syndrome can be calculated to rank the significance of symptoms.The irrelevant and redundant information in the original features would degrade the performance of learning algorithms. Feature selection is needed to extract core symptoms and remove secondary symptoms. In general, a feature is good if it is relevant to the target concept. Here information gain  is used  To extract the core symptoms, different groups of symptoms, whose information gains are above a chosen threshold, are used to train the SVM-based classification scheme. As classifiers can obtain a high accuracy in imbalanced cases which is meaningless, we randomly choose ten positive samples and ten negative samples to constitute the test set, while the rest 110 positive samples and 36 negative samples constitute the training set. For each group of symptoms, the experiment is repeated for 100 times and an average accuracy of prediction in test set is calculated. Finally, the group of symptoms that obtain the highest accuracy is regarded as the core symptoms.Xtrain = {xi, yi}i=1n and a kernel function K, SVM finds the optimal αi for each xi to maximize the margin between the hyperplane and the closet instances to it. The class prediction function for a new test instance x is formulated asαi values are called support vectors. In the case of wind-phlegm collateral obstruction syndrome, the positive support vectors outnumber the negative support vectors as a result of the imbalance of the dataset. According to the prediction function, the diagnosis of a new patient is likely to be dominated by positive support vectors and produce a positive prediction.SVM would be ineffective in determining the class boundary and produce suboptimal classification models when the training instances are imbalanced. And the performance would drop significantly accordingly. Given a set of labelled instances X and Y, Euclidean distance is used to define the similarity between them as later:Here we introduce a strategy of pairwise undersampling to overcome the imbalance. In general, two samples which are similar to each other in core symptoms are merged into one. Given two samples During the implementation, a proper Euclidean distance threshold should be selected for pairwise undersampling. In fact, threshold on the high side would merge the samples excessively and cause loss of information, while threshold on the low side leads to deficient mergence and barely satisfactory improvement of the prediction performance. Thus, we set different distance thresholds to generate several SVM models. For each Euclidean distance threshold, the experiment is repeated for 100 times, and an average accuracy of prediction in the test set is calculated. The SVM model that obtains the highest accuracy is selected. To measure the performance of SVM-based classification scheme, we introduce the metric of G-mean , which iApart from generating diagnostic model for wind-phlegm collateral obstruction syndrome based on the core symptoms, we also attempt to shed light on the molecular basis of the significance of core symptoms for the purpose of a better understanding of the syndrome. The analysis of molecular mechanism is undertaken by Pathway Pattern-based method  and MetaPathway Pattern  is extra Then we related core symptoms with the extracted Pathway Pattern in the reverse direction. A gene is connected with an association rule of the Pathway Pattern if the gene contains all the pathways of the association rule in its related pathways. Similarly, a core symptom is connected with a gene if the gene exists in the associated genes of the HPO terms that have similar meaning with this symptom. In this way, a symptom-gene-pathway multilayer correlation network was constructed using Cytoscape  to discoP value is assigned to each pathway to indicate the statistical significance of the enrichment. The enriched pathways are ranked by −log.On the basis of the relevant genes, we uncovered enriched pathways in MetaDrug platform . A P valP  value [P  value, which is calculated using the basic formula for hypergeometric distribution, essentially represents the probability for a particular mapping of a gene list to a network to arise by chance, considering the numbers of genes in the gene list versus the number of genes in the network. In principle, all the biological networks constitute the interaction network of input genes. However, as the interaction network built from all of the gene nodes might be too large to present; here we use the the P  value to prioritize the biological networks and select the top ranked network as the most relevant interaction network of the input genes, which helps to decipher the molecular basis of core symptoms.The relevant genes were used as the input list for the generation of relevant biological networks in MetaDrug. The algorithm for generating the networks was chosen as Analyze Networks algorithm. The Z-score, G-score, and P  value  are threInformation gains were computed for all of the 102 symptoms on the wind-phlegm collateral obstruction syndrome. Some severely imbalanced symptoms might also obtain high information gains; nevertheless, these symptoms are likely to be valueless in classification. For example, we cannot trust a symptom whose three negative instances all lead to negative outcome because these three negative instances are too few to be convincing. In the extreme situation, these three negative instances might all be divided into the test set. Here we set a threshold of 5 to remove the severely imbalanced symptoms whose minority is less than 5. In this way, 62 symptoms were left for further analysis.  Different thresholds were chosen based on the sorted information gains to select symptoms for the training of classification scheme. Some thresholds were omitted as the selected symptoms did not change much. As illustrated in As illustrated in  Euclidean distances were computed based on the core symptoms for all possible combinations of two samples. Next, the Euclidean distances were ranked to describe the closeness of samples. We set different thresholds to combine two similar samples, which were then replaced by their average. As shown in  To reveal the change of performances of classification schemes, the distribution and tendency of sensitivity and specificity of different models are demonstrated in Further, the metric of G-mean, which has been used for evaluating classifiers on imbalanced datasets, was utilized to measure the performance of SVM-based diagnostic model. As illustrated in We retrieved as many HPO terms as possible for each core symptom manually. For example, as to the symptom “Drowsiness”, we searched the database by keyword “drowsiness” and other synonyms like “somnolence,” and found three HPO terms which convey similar meaning: “Drowsiness” (HP:0002329), “Somnolence” (HP:0001262), and “Paroxysmal drowsiness” (HP:0002329). As shown in Then associated genes of the 43 HPO terms were retrieved and sorted according to their number of occurrences. In total, 775 different genes were retrieved, of which POLG (Entrez Gene ID: 5428) occurs for 12 times and ranks the first, while 353 different genes occur only once and come last. Genes whose occurrences are below 3 are regarded as occasionally occurring genes and are removed from further analysis. As a result, 251 genes were remained and constituted the relevant genes. Next, we prepared pathway information for the relevant genes through searching the KEGG database. As some genes have no pathway information in the KEGG database, the number of records in the pathway dataset is reduced to 159. Based on the pathway dataset, the Pathway Pattern was extracted through bidirectional association rule mining  to reflect the biological features of core symptoms. As shown in In the reverse direction, we related the extracted Pathway Pattern with the 251 relevant genes and then related these genes with the 24 core symptoms. It should be noted that only complete symptom-gene-pathway interactions are maintained to correlate core symptoms with the Pathway Pattern. In other words, a node is removed if it is isolated; an edge is removed if it terminates at the gene node. In this way, a symptom-gene-pathway multilayer correlation network was formulated as The 251 relevant genes were used as the input list for the enrichment analysis by pathway maps in MetaDrug. The enriched biological pathways in P value of 2.66e-37. It contains 25 input genes and one fragment of canonical pathway from IL-6 to STAT2. Some of the input genes, namely, B-Raf, ALK-1, Endoglin, PPARgamma fusion protein, and SOX 10, are significantly upregulated. The biological network helps to explore the molecular basis of core symptoms.We generated relevant biological networks in MetaDrug on the basis of the 251 relevant genes. The number of nodes in a network is set as 100. As shown in In this work, we propose a two-level model for the analysis of syndrome of acute ischemic stroke. The novel method is applied in the case of wind-phlegm collateral obstruction syndrome of ischemic stroke. The two-level model not only selects core symptoms to generate the SVM-based diagnostic model of wind-phlegm collateral obstruction syndrome but also uncovers the underlying mechanism of core symptoms in the molecular level. The molecular mechanism analysis, which contributes to the study of syndrome, is undertaken in three complementary aspects: a symptom-gene-pathway multilayer correlation network is constructed to relate core symptoms with the Pathway Pattern; enriched pathways are revealed which might be responsible for the significance of core symptoms; most relevant interaction network is generated to decipher the molecular basis of core symptoms. We demonstrated the framework of the two-level model by using 166 acute ischemic stroke samples of patients on wind-phlegm collateral obstruction syndrome with 102 symptoms. We showed the process of the generation of diagnostic model. To begin, information gain was computed for each symptom to decide the relevance of symptoms to the syndrome. Different information gains were then used to choose symptoms for the training of SVM-based classification schemes. The group of symptoms which yielded the best classification scheme was selected as the core symptoms. Owing to the imbalance of dataset, the classification scheme performs poorly in predicting negative instances. Through pairwise undersampling, we combined pairs of samples which are close in Euclidean space. A proper distance threshold was chosen to combine the samples in case of excessive or deficient mergence. After these procedures, we generated the SVM-based diagnostic model, which improved the accuracy and G-mean of the prediction. On the basis of core symptoms, the diagnostic model could be used to predict the wind-phlegm collateral obstruction syndrome for acute ischemic stroke patients.P value was selected as the most relevant interaction network to decipher the molecular basis of core symptoms. In combination, the analysis of molecular mechanism sheds light on the underlying mechanism for the significance of core symptoms and contributes to a better understanding of wind-phlegm collateral obstruction syndrome. Next, we demonstrated the flow of molecular mechanism analysis to account for the significance of core symptoms. To bridge the gap between core symptoms and genes, we searched the HPO database for synonymous phenotypic terms and retrieved genes associated with these HPO terms. Then the mechanism analysis was carried out by Pathway Pattern-based method and MetaDrug platform in three complementary aspects. Pathway Pattern-based method was used to build a symptom-gene-pathway multilayer correlation network. The Pathway Pattern was extracted by mining the KEGG pathway entries of relevant genes, which are related with core symptoms. In the reverse direction, the Pathway Pattern, genes, and core symptoms were connected to generate the multilayer correlation network, which presents a new view of the relationship between symptoms and pathways. Additionally, MetaDrug platform was used to reveal the enriched pathways and construct the relevant interaction network. The enrichment analysis by pathway maps was based on the relevant genes. Some of the enriched pathways have been demonstrated to play a role in the pathophysiology of stroke in previous literature. Further the relevant genes were utilized as the seed nodes for construction of relevant biological network. The subnetwork that obtained the highest  The two-level model is a new attempt to extend the analysis of TCM syndromes. Previous research has mainly focused on building up diagnostic models for accurate prediction of TCM syndromes based on selected symptoms. The two-level model moves one step forward to analyse the mechanism of selected core symptoms in the molecular level. It combines the generation of diagnostic model with the analysis of molecular mechanism of core symptoms. Certainly, there are some limitations in our method. Firstly, this study takes only one syndrome of acute ischemic stroke into consideration because of the small and imbalanced dataset. This would affect the reliability and usefulness of the method. Our future work would collect more samples for a comprehensive research on the syndromes of acute ischemic stroke. Secondly, our method can suffer from annotation bias which is also the limitation of most functional annotation-based methods. Associated genes of impulse-related and fur-related symptoms need to be added in future work. Thirdly, laboratory or radiological evidence could be taken into consideration in the future in order to generate more accurate diagnostic model. In summary, this work demonstrates that integration of diagnostic model and molecular mechanism analysis creates an interesting perspective for better understanding of TCM syndromes. It not only makes a contribution to the research on syndrome classification but also provides insights into the molecular mechanism of the significance of core symptoms of syndrome. It would provide a new opportunity for the study of TCM syndromes.