Moving towards the more electric aircraft (MEA), a hybrid actuator configuration provides an opportunity to introduce electromechanical actuator (EMA) into primary flight control. In the hybrid actuation system (HAS), an electro-hydraulic servo actuator (EHSA) and an EMA operate on the same control surface. In order to solve force fighting problem in HAS, this paper proposes a novel linear extended state observer (LESO)-based motion synchronization control method. To cope with the problem of unavailability of the state signals required by the motion synchronization controller, LESO is designed for EHSA and EMA to observe the state variables. Based on the observed states of LESO, motion synchronization controllers could enable EHSA and EMA to simultaneously track the desired motion trajectories. Additionally, nonlinearities, uncertainties and unknown disturbances as well as the coupling term between EHSA and EMA can be estimated and compensated by using the extended state of the proposed LESO. Finally, comparative simulation results indicate that the proposed LESO-based motion synchronization controller could reduce significant force fighting between EHSA and EMA. Actuation system is a key factor in the aircraft, which drives the control surface to manipulate the aircraft’s attitude and flight path . For a lMoving towards the more electric aircraft (MEA), a hybrid actuator configuration provides an opportunity to introduce an electromechanical actuator (EMA) into primary flight control ,5. ThereAdopting the HAS in aircraft also introduces some problems that need to be addressed. It is obvious that EHSA and EMA are of different operating principles, causing different dynamic responses with the same input signal . When boIn order to achieve the synchronous force outputs of two different actuators, the difference between the average actuator forces and the actual actuator force was introduced to an integrator to generate a position demand offset to eliminate force fighting . The resAn effective solution of the force fighting issue between the two different actuators is to introduce the motion state synchronization method. The motion states of those actuators including displacement, velocity, acceleration, jerk and etc. can be maintained in a consistent condition by adopting motion state synchronization . AcquiriThe main contribution of this study is to propose a novel linear extended state observer (LESO)-based motion synchronization control method without increasing any additional sensor. In terms of acquiring the requisite state variables without additional sensors, constructing a suitable state observer is a feasible method. Therefore, the LESO is designed to estimate the state variables including the nonlinear dynamics, uncertainties, and the external disturbances, as well as the coupling term between EHSA and EMA. The difficult issue of obtaining the state signals for motion synchronization controller can be solved by the proposed observer without increasing any sensor. Then the synchronization controllers will be developed according to the observed states to compensate those unknown disturbances and the coupling force by appropriately reassigning the control signals to the actuators. The dynamic force fighting between EHSA and EMA is supposed to be reduced by the proposed ESO-based motion synchronization control method and satisfactory tracking control performance can be achieved by applying the proposed controller.The structure diagram of HAS composed by EHSA and EMA is shown in In thus a hybrid system, EHSA and EMA are controlled by flight control input signals As shown in Usually, the variation range of angular displacement s linear  and can The conventional EHSA typically consists of a servo valve, a symmetrical hydraulic cylinder and other accessories. According to previous studies ,15,16, tThe input flow of hydraulic cylinder can be given as:Then, the flow dynamics and force dynamics of EHSA can be described as:Define Here, A typical EMA, shown in The mechanical dynamics of the EMA can be described as a lumped mass model, all rotating parts of the reduction gear, the ballscrew nut as well as the connection shafts are represented by the following equation:The transition relationship between the rotational part and the translational part can be described as:Define Considering that the control surface is almost rigid, it is reasonable to neglect the dynamics of the control surface. Therefore, ensuring the synchronic outputs of EHSA and EMA is a significant part to maintain the displacement of the control surface The desired motion trajectories are set by the trajectory generator  which isDefine the reference trajectory vector as The overall schematic diagram of the proposed LESO-based motion synchronization controller is shown in As stated earlier, the main aim of this work is to eliminate force fighting in HAS by designing a motion synchronization controller based on motion states of EHSA/EMA. Therefore, a state feedback linearization controller is essential to achieve the expected motion state synchronization. Indeed, it has been shown that the state space models To this end, the following state feedback linearization controller is presented as:Applying the control laws Equations (14) and (15) to Now, we can design the new inputs Applying controllers Equations (18) and (19) to EHSA and EMA respectively leads to the motion state synchronization of EHSA and EMA, because all motion states of EHSA and EMA will asymptotically track the same reference trajectory However, from the control laws Equations (18) and (19) for EHSA and EMA, the control laws require all the state variables of EHSA and EMA, i.e., displacements, velocities and accelerations. However, only the displacement sensors, which could measure the angular displacement of the control surface or the linear displacement of the cylinder, are available for the practical aviation actuation system on the aircrafts. Therefore, a specifically designed state observer is required to provide the requested immeasurable state variables.To this end, the ESO will be designed for EHSA and EMA to observe system state variables along with the uncertainties in the next subsection.In this subsection, LESO will be designed for EHSA and EMA respectively, to estimate the uncertainties and the motion states of the actuation systems. The ESO regards all factors affecting the plant, including nonlinearities, uncertainties, and disturbances as a total uncertainty  which needed be observed ,20,21. TFor EHSA system Let the extended state of ESO The extended-order system Now, we can design the ESO for The ESO given in Equation (24) is a linear one, i.e., LESO, and it has several advantages. The structure of LESO is simple and is easy to be implemented on the practical servo system. Then, the observer gain Now we can use the same technique to design LESO EMA. Firstly, the EMA system Similar to EHSA, we can design the LESO for In order to synchronize the motion states of EHSA and EMA, LESO-based motion synchronization controller will be designed to make sure that EHSA and EMA tracking the same reference trajectory For EHSA, based on state feedback linearization controller Equations (14) and (18), the motion synchronization controller can be designed by utilized the state observation results of LESO Equation (24) as:With the controller Equation (28) and the observer Equation (24), the closed-loop stability of EHSA system Rewrite EHSA system Noting that By using the same controller design technique, it is easy to design the motion synchronization controller for EMA base on state feedback linearization controller and LESO Equation (27), which is given as:Then the motion synchronization controller (30) of EMA can be rewritten as:The stability analysis of the closed-loop control system with the proposed LESO-based motion synchronization controllers will be discussed in this part.Defining the state tracking error vector of It is obviously that the following equation holds:Then, considering Equations (29), (30) and (36), the state tracking error dynamics can be expressed as:According to Equations (23) and (24), the observer error dynamics Combining Equations (37) and (38) leads to:The closed-loop stability of EHSA system Since the pair With the LESO Equation (27) and the controller Equation (31), the closed-loop stability of Similar to the EHSA system, the same results about the stability of the closed-loop EMA system can be concluded.In order to verify the advantage of the proposed LESO-based motion synchronization control algorithm, it is implemented on a simulation model of HAS of large civil aircraft. The simulation is established in MATLAB/Simulink environment and the parameters of the simulation model are listed in The parameters of the proposed motion synchronization controller are calculated as follows: Controller C1:the classical PID control with parameters: EHSA: Controller C2:the state-difference feedback approach proposed by Cochoy et al. . In thisController C3:the proposed LESO-based motion synchronization controller.In addition, in order to illustrate the performance of LESO-based motion synchronization controller more objectively, the following three control algorithms will be compared in two simulation scenarios:In this simulation scenario, the input command The input command from flight control system and tracking responses with three compared controllers C1–C3 is shown in The force fighting results of three compared controllers C1–C3 is shown in The comparative simulation also runs with dynamic signal input command In order to verify the control performance of the proposed controller under unknown disturbance, the comparative simulation runs with real flight control command under random perturbance. In this simulation scenario, the input command of the HAS is set as a piece of real flight control command, which is shown in In this simulation, the external air disturbance force is set as a proportional load, i.e., This simulation indicates that the proposed LESO-based controller can achieve good dynamic command tracking performance under nonlinear and unknown perturbance.LESO-based motion synchronization control strategy has been proposed in this paper for hybrid actuation system of more electric aircraft. All motion states of EHSA and EMA can be observed by the well-designed LESO and the motion synchronization controller can ensure EHSA and EMA to simultaneously track the desired motion trajectory from flight control system. As a result, the force fighting can be greatly reduced due to synchronous motion of EHSA and EMA. The robustness of the closed-loop system is guaranteed by introducing the forth state of LESO, because the forth state of LESO can estimate and compensate the lumped effect of nonlinearities, uncertainties and unknown disturbances. Finally, the effectiveness of the proposed LESO-based control strategy is validated by comparative simulation results under different conditions.


The review of the need of fasting for lipid profile analysis  is based on the followinggrounds:Since the postprandial state predominates during most of the day, thepatient is more exposed to the lipid levels in this condition whencompared with the fasting state. Therefore, the postprandial state mayrepresent more effectively the potential impact of the lipid levels onan individual's cardiovascular risk.Measurements in the postprandial state are more practical and provide thepatient a greater access to the laboratory, decreasing the number ofmissed working days and medical appointments due to missed tests,allowing a better assessment of the individual's cardiovascularrisk.Blood collection in the postprandial state is safer in severalcircumstances and may help prevent hypoglycemia secondary to the use ofinsulin in patients with diabetes mellitus, or due to prolonged fastingin pregnant women, children, and elderly individuals, minimizingcomplications and increasing the adherence to the tests and theattendance to medical appointments.7There are no significant differences in measurements of totalcholesterol, HDL-C, non-HDL-C, and LDL-C performed in the postprandialor fasting state. Levels of TG increase in the fed state, but suchincrease has little relevance as far as a regular meal without fatoverload is considered, with the possibility of adjustment in thereference values.With a flexibility for lipid profiling, there is a greater amplitude ofschedules, thereby reducing congestion in the laboratories, especiallyearly in the morning, bringing more comfort to the patient.With the technological advances in diagnostic methods, the main assaysavailable have mitigated the interference caused by increased sampleturbidity due to high TG concentrations. However, there are potentiallimitations, especially related to the calculation of LDL-C, in whichperformance studies of different methodologies have shown a need for arevision of the practical use of the adopted formulas.In easing the requirement for fasting in the collection of samples for lipidprofile assessment, some clinical and laboratory recommendations becomeimportant.Nonfasting sample collection for lipid profiling: may be done by thelaboratory with the presence of the information about fasting at thetime of sample collection in the laboratory report.A medical request without a definition of the fasting duration andwithout other tests known to require fasting: it is recommended toinclude in the laboratory report the fasting duration informed atthe time of the sample collection.Presence in the same request of other tests that require fasting: thelaboratory may define that the lipid profile should be collectedwith a 12-hour fasting when other laboratory tests, ordered on thesame request, also require this period of fasting. The laboratory isrecommended to specify whether or not fasting is required for eachexam: no fasting, 12-hour fasting, or according to the definitionset by the laboratory.Sistema de InformaçãoLaboratorial) may be used, based on the information ofthe time elapsed since the last meal.When an indication of a specific fasting duration is present: if therequest by the physician has a specific fasting duration, thelaboratory should follow such recommendation. The calculation ofhours of fasting by the "SIL"  according to the cardiovascular riskassessment estimated by the prescribing physician are described inInsertion of a note in the report referencing that the lipid profileresults should be interpreted according to the clinical assessmentand evolution of the patient. The following sentence is recommended:"The clinical interpretation of the results should take into accountthe reason for indication of the test, the metabolic state of thepatient, and the stratification of risk for establishment of thetherapeutic goals."11The target reference values of the lipid profile for children andadolescents are shown in Patients with diabetes and no risk factors or evidence of subclinicalatherosclerosis should maintain LDL-C levels below 100 mg/dL.Patients with risk factors or subclinical atherosclerotic diseaseshould maintain LDL-C levels below 70 mg/dL. Patients with a historyof acute myocardial infarction; stroke; coronary, carotid orperipheral revascularization; or history of amputation shouldmaintain the LDL-C levels below 50 mg/dL.14The inclusion of a specific note about the screening of familialhypercholesterolemia (FH) is left at the discretion of thelaboratory. The following sentence is recommended: "Values of totalcholesterol > 310 mg/dL in adults or ≥ 230 mg/dL inchildren and adolescents may be indicative of familialhypercholesterolemia, if secondary dyslipidemias areexcluded."13 The following recommendations are suggested tothe laboratories:The assessment of LDL-C can be performed by direct measurement or estimated bycalculation based on Friedewald's or Martin's formula.15 is used to estimate LDL-C; in these cases,Martin's formula16or direct measurement should be used.Observe the limitations of nonfasting and TG values > 400 mg/dLwhen Friedewald's formula16When collecting postprandial samples, the LDL-C measurement can beperformed by direct measurement or calculated using Martin'sformula.Include non-HDL-C in the calculation along with other results of thelipid profile in adults, even without fasting, since the TG levelsdo not interfere in such calculation. Reporting or not of the VLDL-Ccalculation may be done at the discretion of the laboratory.The main purpose of this document is to standardize the clinical and laboratoryactions in regards to the flexibility of fasting in the lipid profile analysisacross the national territory, contributing to offer security to thedecision-making process by physicians and laboratories, grounded by scientificevidence.


This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon’s entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed. Defect detection plays a vital role in automatic inspection in most production processes . In many of these processes, quality controls still depend to a large extent on the training of specialized inspectors. Manual inspection involves limitations in terms of accuracy, coherence and efficiency when detecting defects. This is due to the fact that inspectors are prone to suffer fatigue, boredom or simply fail to pay sufficient attention because of the repetitive nature of their tasks . To dealTexture analysis provides a very powerful tool to detect defects in applications for visual inspection, since textures provide valuable information about the features of different materials.In computer vision, texture is broadly classified into two main categories: statistical and structural . TextureOn the other hand, structural textures, also called patterned textures, are characterized by a set of primitives (texels) and placement rules. The placement rules define the spatial relationships between the texels, and these spatial relationships may be expressed in terms of adjacency, closest distance or periodicities. The texels themselves may be defined by their gray level, shape or homogeneity of some local property  . StatistMore recently, Ngan  providedIn a recent and complete review of defect detection in textures, Xie  classifiClustering techniques are used in many defect detection methods; these methods are mainly based on the extraction of texture features. Such features are obtained using different techniques, such as co-occurrence matrix ,26,27,28Spectral-approach techniques provide either the frequency contents of a texture image (Fourier transform) or spatial-frequency analysis ).Fourier transform shows good results when applied over texture patterns with high directionality or regularity, because the information about the directionality and periodicity of the texture pattern is well recognizable in the 2D spectrum, although it fails when attempting to determine the spatial localization of such patterns. Concerning spatial localization, Gabor filters provide better accuracy, but they show a lack of reliability when processing natural textures, since there is no single filter resolution that can localize a structure. The main advantage that WT has over the Gabor transform is that it makes it possible to represent the textures in the appropriate scale because of the variation of the spatial resolution it provides.The suitability of WT for use in image analysis is well established: a representation in terms of the frequency content of local regions over a range of scales provides an ideal framework for the analysis of image features, which in general are of different sizes and can often be characterized by their frequency domain properties . This maWavelet transform has resulted in two groups of techniques for detecting defects: (1) direct thresholding methods ,45,46,47In the present paper, a direct thresholding method was decided to be used due to the several disadvantages that methods based on feature vectors present (high computing costs and difficulties to set stop criteria from the proximity-based techniques or training difficulties posed by learning-based techniques). If direct thresholding methods are used, a couple of important challenges have to be faced: (a) how to select the optimal wavelet decomposition level; and (b) determining which bands enable the elimination of the maximum information relative to the texture pattern, which involves two dangerous risks: (a) features of the texture pattern can merge with those of the defects due to an excessive wavelet decomposition; and (b) false positives may appear due to a defective reconstruction scheme.A review of the literature on texture defect detection methods using wavelet transforms shows a great deal of interest in methods for automatic selection of the optimal wavelet decomposition level due to the potential industrial applications. Of these, the best performance is offered by the methods proposed by ,46,47, tA numeric analysis of the normalized absolute function value (NABS) at different wavelet decomposition levels, which makes it possible to determine the texture directionality. The proposed algorithm makes it possible to classify into high and low directionality patterns, depending on the variation in the slope of the NABS.A novel use of the normalized Shannon’s entropy has been formulated, calculated over different detail subimages, in order to determine the optimal decomposition level in textures with low directionality. For this purpose, it is proposed to calculate this optimal decomposition level as the maximum ratio between the entropy of the approximation subimage and the total entropy, as the sum of entropies calculated for every subimage. This ratio provides better results when detecting defects in a wider range of textures.This paper proposes a new approach to face the aforementioned inconveniences based on the normalized absolute function value (NABS) and Shannon’s entropy calculation. The main contributions can be summarized as follows:This article is organized as follows: L; and a band-pass filter: H), and by taking, from the resulting image, a sample out of two. This process is applied to all the rows, and then, with the resulting rows to all of the columns, provides four subimages defined as L filter on rows and columns), H filter on rows and L filter on columns), L filter on rows and H filter on columns) and H filter on columns and rows). These four subimages share the same origin, and their size is a quarter of the original size. One of them  was used to separate patterns of structural textures with high directionality from the rest. To determine the directionality of a texture, the values of the NABS of the detail subimages  of the horizontal, vertical and diagonal detail subimages in level quations –4):(2):(2)j arThe graphs to right of each image show the NABS values of the horizontal, vertical and diagonal details , so thaF will be derived from the composition of the rest of detail subimages. In the case of the textures in The resulting image As regarding the behavior of the first-order coefficients of the trend line equation on texture patterns shown in In the process described in It is already known that Shannon’s entropy describes the level of randomness or uncertainty in an image, i.e., how much information such an image provides. It is possible to state that the higher the value of the entropy, the greater the image quality . Figure T values on which the entropy function is applied and where the function X (256 gray-level images) consists of these 256 possible values and the probability The entropy value ,58 is caj, the values of each subimage are firstly transformed over the range of integer values between zero and 255. Therefore, in addition to allowing the visualization of the resulting subimages, entropy can be calculated, as well. For each decomposition level, Shannon’s entropy provides four values: j. Such values are referred to as In performing the convolution process of the image with a specific wavelet signal, unbounded real values, positive and negative ones, are obtained. Thus, in order to calculate Shannon’s entropy function in each subimage and for each decomposition level The function value of Shannon’s entropy indicates how much information on the texture from the original image remains in each subimage. This function value is calculated on each subimage derived from each decomposition level. According to Equation , it can In an optimal decomposition level, it would be possible to eliminate the texture pattern with no significant loss of information on the defects. In order to determine the optimal decomposition level, a ratio value j and that it disappears at level Variations of this ratio allow detecting changes in the amount of texture information between two consecutive decomposition levels. The goal is to find the decomposition level causing the maximum variation of the value expressed in Equation  with regFor this calculation process, the coefficient The optimal decomposition level F and the thresholding method based on computing the minima of the maxima of the histogram (MiMa) .Clustering-based methods: Ridler’s method , TrusselEntropy-based methods: Pun’s method , Kapur’sTo answer the above questions, the following thresholding methods were selected, attending to the three first groups proposed by Sezgin:In order to perform a quantitative analysis of the segmentation method proposed in this paper, the adequate metrics are necessary to be selected. From the set of metrics proposed by Sezgin  and ZhanME is calculated as the relation between the pixels of the test image (segmented by means of the method proposed in this paper) and those of a pattern image . Equation (Equation  is used Equation  is used Compute Shannon entropy: Compute β indicates the number of instructions executed by function MbufPut2d of the Matrox Imagin Library (MIL). Therefore, the computational complexity of the EADL method is From above individual computational complexities, a total computational complexity of order The EADL method, whose algorithm is shown in After checking different mother wavelets , the Haar-based function with two coefficients has been used as the mother wavelet, because it shows the best ratio between yield and computational cost. The Haar wavelet has been applied until the fourth decomposition level. Higher decomposition levels have proven to lead to the merger of the defects with the texture pattern, which prevents their segmentation.The Kapur method for natural directional textures wood  the band, using NABS and (2) the optimal wavelet transform resolution level, using Shannon’s entropy, has been used.Valuable information about the directionality of the texture patterns can be extracted from the analysis of the NABS value of the horizontal, vertical and diagonal details at different decomposition levels.A correct wavelet reconstruction scheme has been implemented to remove the texture patterns and highlight the defects in the resulting images.It is demonstrated that the optimal decomposition levels computed from the Shannon entropy are lower than the ones provided by other methods based on the co-occurrence matrix (Han method) or on energy calculation (Tsai method). This fact implies an increment of information in the image resulting from the wavelet reconstruction scheme. This characteristic, together with the optimal selection of a thresholding method (MiMa), has allowed the EADL method to achieve high performances in defect detection: a An analysis of the results of the EADL method with nine different thresholding algorithms showed that selecting the appropriate thresholding method is important for achieving optimum performance in defect detection. On the basis of a metric analysis of 223 images, the most appropriate thresholding algorithm for each texture is proposed. The MiMa method proved to be the most appropriate for the textures, such as painted surfaces, cast metal and fabric. However, the Kapur method has been demonstrated to be better with wood, sandpaper surface, wool, milled surface and bamboo weave.


Dear Editor,A 32-year-old female patient who was pregnant with twins presented for a regular prenatalcheckup with her obstetrician at 25 weeks of gestation. It was her second pregnancy, andshe had carried the first pregnancy to delivery. She was asymptomatic. Ultrasound showedthat one twin was morphologically normal and that the other was hydropic, withinvolution of the brain and only the most rudimentary cardiac tissue .-3. Multiple pregnancies are subject to variouscomplications, the rarest of which is an acardiac fetus, a complication seen in only 1%of all monochorionic twin pregnancies. Although the pathophysiology of an acardiac twin is not wellknown, it is believed that there are vascular anastomoses that divert blood from themorphologically normal twin to the acardiac twin, a condition known as twin reversedarterial perfusion. The acardiac twin almost always presents involution of the brain,together with the absence or malformation of other organs (-6.Recent studies have highlighted the importance of imaging examinations in fetalmediciner organs . The nor,6.Approximately 20% of acardiac fetuses have vestiges of cardiac tissue or a rudimentaryheart. Therefore, it would be correct to call them pseudoacardiac fetuses. That makesthe case reported here even more rare, because it involves a pseudoacardiactwin:monochorionic twin pregnancy; reverse flow in the umbilical cord and descending aorta;presence of arterio-arterial anastomoses; and partial or complete absence of the heartin one of the fetuses. An acardiac twin can sometimes be confused with a teratoma. Thetwo can be differentiated by identifying the umbilical cord and some degree oforganization of the body of the acardiac fetus.The morphological diagnosis of an acardiac twin is made by fetal ultrasound and is basedon the following criteria,7.In 50-75% of cases of an acardiac twin, the use of the watchful waiting strategy isassociated with the death of the structurally normal twin, due to heart failure andhydrops fetalis. In the case presented here, the pregnancy was monitored to term throughthe use of serial examinations, and there were no complications for the structurallynormal fetus or for the mother. The treatment, when necessary, is still controversial.It involves blocking the blood flow to the acardiac twin if the structurally normal twinshows some impairment. The main surgical techniques are aimed at occlusion of theumbilical cord-by ligation with a suture, clamping with bipolar forceps,photocoagulation, or ligature/section of the cord—or obliteration of the circulationwith absolute alcohol. The survival rate for the structurally normal fetus can be ashigh as 75% when some intervention is implementedAn acardiac fetus is a rare complication of multiple monochorionic pregnancies and can bediagnosed through the use of a widely accessible method. Early identification of anacardiac twin can avert a fatal outcome for the structurally normal twin.


In a distributed population of microbes, the impact of community composition on spatio-temporal dynamics was characterized and compared to simulation results using a modified reaction-diffusion model. After introducing a single term to account for crosstalk between each pair of signals, the model was able to reproduce the activation patterns observed in experiments. We quantified the robustness of signal propagation in the presence of interacting signals, finding that signaling dynamics are largely robust to interference. The ability of several wild isolates to participate in AHL-mediated signaling was investigated, revealing distinct signatures of crosstalk for each species. Our results present a route to characterize crosstalk between species and predict systems-level signaling dynamics in multispecies communities.In multispecies microbial communities, the exchange of signals such as acyl-homoserine lactones (AHL) enables communication within and between species of Gram-negative bacteria. This process, commonly known as quorum sensing, aids in the regulation of genes crucial for the survival of species within heterogeneous populations of microbes. Although signal exchange was studied extensively in well-mixed environments, less is known about the consequences of crosstalk in spatially distributed mixtures of species. Here, signaling dynamics were measured in a spatially distributed system containing multiple strains utilizing homologous signaling systems. Crosstalk between strains containing the  In nature, bacteria are commonly found in spatially heterogeneous mixtures. Within these environments, multiple species communicate using chemical signals, and crosstalk often governs the activities of microbial populations, including interactions with the host system, forming biofilms, and bioluminescence. Understanding such bacterial interactions is essential to control and prevent these population-level behaviors regulated by signal exchange. Additionally, quantifying bacterial crosstalk will help improve the robustness of synthetic cellular networks that utilize signal exchange.Although cellular signaling is understood in well-mixed systems with one signal, we lack a detailed understanding of signaling in spatially distributed cellular networks or networks with multiple signals. We created an experimental system to observe and quantify microbial crosstalk between three bacterial languages. A mathematical model was implemented to predict the consequences of the exchange of multiple signals within cellular networks and good agreement between the experimental results and theoretical predictions was observed. In the mathematical model, a single parameter was sufficient to account for crosstalk between bacterial species. These experimental and theoretical tools enable us to better understand and predict how signaling influences the behavior of both natural and synthetic microbial communities. The receptor enacts global changes in gene expression in response to high concentrations of AHLs. Over 150 quorum sensing systems have been characterized [Microbes communicate with each other in order to coordinate behavior and gene expression through a process known as quorum sensing. Several Gram-negative bacteria use acyl-homoserine lactones (AHLs) as a signal to communicate –6. Thesecterized ,8, with cterized ,8, and 5cterized . Variantcterized ,7. VariaChromobacterium violaceum, a pathogenic Gram-negative bacteria that produces a shorter chained AHL, activates gene expression in Vibrio harveyi, a Gram-negative marine bacteria that produces a longer chained AHL [Several examples of crosstalk between signaling microbes, in which signal produced from one species binds to the receptor of a second species, have been reported ,6,9–14. ined AHL ,9,15–19.lux and las circuits. Other studies such as McClean et al. [Chromoacterium violaceum to purified variants of AHL. Although diffusion plays a role here, there was no significant quantitative work done to capture the spatial effects. In another study Dilanji et al. [E. coli colony to the middle of the plate capable of synthesizing AHL molecules, as shown in Here we develop and implement a signaling assay using sender, receiver, and interactor strains to measure signaling dynamics in populations containing multiple AHLs, see n et al.  observedi et al.  looked aThe exchange of multiple AHLs has been reported ,26, but lasI or rhlI synthase genes. The plasmids encoding these constructs are shown in Figure A in Our experimental setup was based on a LuxI/LuxR sender-receiver type plate assay ,25,27,28Receiver cells containing a LuxR regulated fluorescent reporter were distributed on a 20 mm diameter LB agar pad containing a sender strain colony in the middle of the plate, see As shown in As mentioned previously , there aluxI were added to the lawn of receivers to verify a decrease in activation time, by 0.75 hr, for an interactor strain producing the cognate signal. In a negative control where the interactor strain is the wild type E. coli host strain not producing any AHL signal, the activation time is unchanged. This result indicates that the space taken up by interactor cells did not affect the response of the receiver cells , nT, will approach a saturated cell density of s.In this section, we build a mathematical model to explore the correlations of the micro level binding of a signal to a receptor, and the macro level spatiotemporal patterns of gene expression in a system incorporating quorum sensing crosstalk. In previous work done by ,28,30–32ρb per cell. The sender AHLs, with a concentration of cs, diffuse away from the cells with a diffusion coefficient of Dc and are degraded by the media at a rate of da. ρ accounts for an increase in AHL production in the presence of signal. For the senders, the activity (A) defines how this activated rate of signal production, due to changes in production of the synthase protein, depends on the concentrations of multiple AHLs.Initially the senders produce the AHLs at a basal rate of i is the index to describe the type of AHL, ci is the concentration of the ith AHL, P(ci) is the probability of an AHL binding to the receptor, wi is the weight parameter and j is the total number of interacting AHLs. The probability of binding accounts for differences in the binding affinity of each signal variant to the receptor, as well as the competition for multiple signals to bind to the same receptor. The number of receptors (g) changes from a basal level of 100 to 600, as gene expression levels increase due to signal accumulation. To model this smooth transition we used a Hill’s function, see Table A in In the presence of an interacting AHL, the transcriptional activity will be modulated due to the binding of AHLs to the LuxR receptors. Each signal has a variable influence on the activity, both in the ability to bind to a receptor and the downstream influence of such binding on the expression of quorum sensing controlled genes. The ability of an AHL to bind to the LuxR receptor depends on the binding energy and the local concentration of each AHL. In simulations, we considered the probability of an AHL to bind to a receptor and introduced a weight to account for the downstream influence of each AHL variant on gene expression. Therefore, the activity takes the form of,wi) relates the number of AHL-receptor complexes to the extent of gene regulation, with large positive weights indicating that complexes formed by that AHL lead to strong upregulation of quorum sensing regulated genes while weights close to zero lead to inhibition of these genes. The weight is determined by the affinity of the bound receptor for the promoter region of quorum sensing regulated genes, the efficiencies of transcription and translation of quorum sensing regulated genes, and the rate of dissociation for the AHL-receptor complex. A deterministic Boltzmann weight approach was applied to calculate the receptor binding probabilities from AHL concentrations and receptor binding energies [The weight parameter  for the interactors was zero and signal production occurred at a basal level,7 sender cells were added to the inner circle. The inner circle was assumed to have an initial AHL concentration of 70 nM. The outer circle has a variable mixture of receivers and interactors distributed evenly in space. In all cases, there were 108 receivers cells. The initial concentration of the interactor AHL in the outer circle was 70 nM [The constitutive production rate was assumed to have the same value as the maximum production rate of the senders. These equations were solved using the finite difference method. The model predictions were obtained considering the transient behavior of the AHL concentrations. In simulations, we considered two concentric circles; the inner circle has a radius of 1 mm while the outer circle has a radius of 10 mm. The cell densities are governed by Eq  and 3),,3), and as 70 nM . In simuP(cS) is the probability that the AHLs from the sender will bind to the receptor, P(cint) is the probability that the AHLs from the interactor will bind to the receptor, ws is the weight associated with the sender AHL and wint is the weight associated with the interactor AHL. The activity of the receivers modulates the production of the fluorescent gene reporter (RFP), as the reporter gene is transcribed by a promoter regulated by signal bound receptor.Based on the diffusive gradients of signals created by the senders and the interacting species, the activity of the receivers was calculated using,The level of activity of the receivers acts as an indicator of changes in gene expression resulting from the crosstalk. Therefore, we define a threshold activity level for the activation of gene expression and used Eq  to track1) for the signal 3-oxo-C6-HSL binding to the LuxR receptor was fit using the experimental data for no crosstalk. Non-linear least square fitting method was used for this purpose, see Figure Ia in To obtain signal weights of the sender AHL and the two interacting AHLs, simulation results were fit using the experimental data from The validated model of crosstalk has enabled the exploration of the robustness of signal propagation. Signal propagation in the presence of variable levels of crosstalk was simulated for both the LasI and RhlI interactor strains. Using the model, we predicted the sensitivity of signaling dynamics to changes in model parameters, including cell growth rate Click here for additional data file.


The field of metagenomics, defined as the direct genetic analysis of uncultured samples of genomes contained within an environmental sample, is gaining increasing popularity. The aim of studies of metagenomics is to determine the species present in an environmental community and identify changes in the abundance of species under different conditions. Current metagenomic analysis software faces bottlenecks due to the high computational load required to analyze complex samples.A computational open-source workflow has been developed for the detailed analysis of metagenomes. This workflow provides new tools and datafile specifications that facilitate the identification of differences in abundance of reads assigned to taxa (mapping), enables the detection of reads of low-abundance bacteria (producing evidence of their presence), provides new concepts for filtering spurious matches, etc. Innovative visualization ideas for improved display of metagenomic diversity are also proposed to better understand how reads are mapped to taxa. Illustrative examples are provided based on the study of two collections of metagenomes from faecal microbial communities of adult female monozygotic and dizygotic twin pairs concordant for leanness or obesity and their mothers.The proposed workflow provides an open environment that offers the opportunity to perform the mapping process using different reference databases. Additionally, this workflow shows the specifications of the mapping process and datafile formats to facilitate the development of new plugins for further post-processing. This open and extensible platform has been designed with the aim of enabling in-depth analysis of metagenomic samples and better understanding of the underlying biological processes.The online version of this article (doi:10.1186/s12864-016-3063-x) contains supplementary material, which is available to authorized users. The purpose of metagenomics is to identify the species present in an environment. Different types of studies can be performed based on metagenomics. Some examples include the analysis of changes in the presence of species in a given environmental sample and the use of phylogenetic analysis to follow up the spread or determine the origin of a species. A large number of tools are emerging in the form of stand-alone programs (e.g. MEGAN ), interoMEGAN performs taxonomic analyses of a metagenome by mapping reads to different taxa based on BLAST  search rThe number of comparative metagenomic tools is the key point of the metagenomic RAST (MG-RAST) platform. MG-RAST builds clusters of proteins at a given percentage of identity level using QIIME . Once buRecently, a new DNA sequence analysis workflow called META-pipe  has beenThe fine-grained metagenomic analysis workflow developed by our group can operate over a user-defined collection of genomes -thus accelerating the computational process- and with the advantages of being able to map reads over unannotated regions of genomes. Our software provides different mapping methods and different mapping alternatives apart from the best read-genome matching. In addition, it provides information about the quality of mapping and about differences between mapping options. Unlike the methods currently available, which deduce that a species is not present in a sample when its abundance is low (in number of reads), the proposed method can detect low-abundance species by finding reads mapping to particular specific regions of genomes. In addition, the developed workflow is an open platform composed of an expandable set of separate modules that use well-defined format datafiles. This enables the easy on-demand incorporation of new processing tools. Along with low-abundance species support, other tools have been included to verify the correctness of taxonomic assignation and extrapolate DNA sequencing data to gene expression levels.The designed workflow see Fig.  includesIn the line to offer a broad scope of the presented software, the proposed workflow can handle sequences of different length obtained with different sequencing technologies  and post-processing procedures, which require the following input: (1) Sequence comparison files, (2) taxonomic description of the reference dataset, and (3) annotation files for the genomes .(1) Sequence comparison files: the workflow has been designed so that it is compatible with any sequence comparison software . The default comparison software used in this workflows is GECKO, however, the user can employ other packages. To include other comparison software a format conversion program would be needed. A parsing conversion system for BLAST is already included in the workflow. The parsing module converts sequence comparison files to a format composed of headers (read-genome tuples) followed by rows, where each row represents a fragment for the tuple. Fragments belonging to a read-genome match are defined by a 12-tuple: k is the k−th fragment reported by the read n A taxonomic description file allows the customization of hierarchical relationships between organisms in the reference database as assigning strain relationships between species or separating strains that belong to a common ancestor. Such file can be generated automatically using a module of the workflow and/or can be manually built to insert customized relationships between species. The format of the file generated is a text file including a 5-tuple per line, each tuple being a new genome: n and m are the specie and subspecie id’s. These can be used to set up custom boundaries. For further details, please see “Taxonomy files” in the Additional file Where mentclass2pt{minim(3) Annotation files are used to carry out all coding region-related computations in the post-processing phase. Therefore, these files are optional and should be included if annotation post-processing modules are to be used. As in the case of comparison software, a parsing system is implemented; e.g. a parsing system for GenBank’s annotation files has been included in our workflow.Comparative analysis of metagenomes is an expensive computational process that involves comparing a large set of DNA fragments against an enormous database of candidate sequences . It should be noticed that, by definition, bacteria in the metagenome are uncultured species and the sequences in the databases that already exist are not –most likely– the corresponding to the species in the metagenome. Even some large mutations  can happen regularly. Therefore a more flexible matching is proposed, which differs from an assembly by mapping in which there are quite close representatives of the sequenced bacteria.Thereby we included the option of using a custom glocal  alignmenOnce local alignments are calculated , fragments are extended by joining those that are close enough according to a given maximum-gap parameter. This is done by calculating the Needleman-Wunsch matrix between the start and the end of the matching read within the genome region with a customized implementation.Furthermore, glocal alignment can be performed by combining the local alignments produced by alignment tools such as BLAST or GECKO with the provided custom glocal alignment. All parameters can be user-defined, thus providing data processing flexibility. Table The extension of fragments requires the re-calculation of fragment scores to identify the best match out of a list of candidates during the mapping process. The properties of the extended fragment, namely length –of bases–, number of identities and inserted gaps stand for the raw score. The raw score has to be normalized in order to obtain the expected value of a reported fragment. This is performed using K and Lambda parameters using a similar approach to that of the BLAST family. K and Lambda parameter are calculated as described in .To compute the raw score of the extended fragment produced by our custom glocal alignment we apply a traditional affine scoring model , as shown in the following formula: RS stands for “Raw Score”, I for the total number of identities in the fragment, Mr for the match score, L for the total length of the fragment in base pairs, Gi for the total number of open gaps in the fragment, Ge for the total number of extension gaps in the fragment, Mp for the mismatch penalty, Pi for the penalty of an open gap and Pe for the penalty of an extension gap.Where The mapping module (GMAP) process offers a three-level mapping option that not only discovers highly abundant species that hide others in terms of abundance due to high similarity or uncertainty in the alignment, but to also obtain quality distance measurements between the best 3 candidates for every match. The top three candidates are selected based on identity and coverage thresholds and expected values. Moreover, users can perform different mappings by restraining subsets of reads using different thresholds.In a scenario with a highly abundant organism, further analysis can be performed by only considering certain genomes using certain options e.g. to observe differences and extract statistical indicators of close candidates.Coverage threshold phase: The length of the match divided by the length of the read.Identity threshold phase: The number of identities in the match divided by the length of the match.Filtering step: A filtering step allows the researcher to consider only a subset of reported fragments, enabling a levelling up mapping method. If a fragment does not reach pre-filtering thresholds, it will be discarded. Such filtering allows a two phases pre-filtering: Repeat this step for 3-option mapping and, if fragments are still active: Select the fragment with the smallest expected value and if it is lower than the maximum allowed expected value. This fragment is included in the mapping file as first, second or third candidate depending on the number of options chosen and the genome is inactivated for the next option iteration.If no more fragments are still active or none of them exceeds the thresholds, the read is decided with either no mapping option or up to 3 mapping options.Every read yields a list of reported fragments to which the following algorithm is applied. See “Mapping decision and fragments” in the Additional file Rather than developing a monolithic application with graphical interfaces, we opted for a simple pipelining procedure in which new software modules can be used to exploit results. To facilitate user interaction, a complete web-based interface has been developed based on Galaxy workflow manager, which enables users to easily run their analyses in both a local instance or in dedicated servers. In addition, a User Guide  is availThe results given by our workflow software are illustrated by an experiment where two collections of 6 metagenome samples each were extracted from faecal microbial communities of adult female monozygotic and dizygotic twin pairs concordant for leanness or obesity and their mothers . Raw datThe analysis of the species present in metagenomic samples enables taxonomic classification based on abundance of mapped reads. Information about the species present in metagenomes and variations across a collection of species is yielded by GMAP in Comma Separated Value (csv) format files that can be edited using common spreadsheet software see Fig. a.Fig. Abundance data are primarily used to determine the species that are present in a metagenomic sample and can be exploited in comparative studies on the over- or under-abundance of species in different samples. However, abundance data does not provide information on the quality and certainty of mapping. This lack of reliability can be partially compensated by using the n-mapping method.Three-options mapping analysis Our software has the ability to perform the mapping of reads through a multiple-level strategy. After the best read-genome mapping value is used, the used fragment is inactivated and the genomes belonging to different strains of the same species are optionally inactivated, and the process is repeated. This way, we get the second, third and subsequent best read-genome mapping values. A long separation between the mapping options provides stronger evidence supporting the validation of the mapping procedure.When comparing 3-mapping options, the detection of peaks in second or third options means that a particular species is repeatedly the second or third candidate see Fig. b. TheseIn addition, the 3-mapping approach allows to assess the mapping certainty at both reads and species level; at read level by comparing fragments quality indicators of particular genomes against the rest see Fig. d, and aOn the other hand, in a joint analysis of the Fig. Figure Fine-grained tuning and closer examination In a scenario where a specific species has been the second option a higher number of times compared to the first option, as discussed in the previous Section, the mapping should be exhaustively analyzed and compared with other species. Such analysis would provide more certainty of the presence of a low-abundance genome by checking the properties of its matches, and would enable contrasting the variances in the matches between a high-abundance genome and its best second option. Moreover, it is possible to perform a one vs. one, one vs. some, or, some vs. some comparative analysis of the target species. This type of analysis can be performed based on any of the properties of the mapped reads, such as length, similarity, coverage, or any user-defined properties. This information is particularly useful when the first and second mapping options identify different species .Figure Statistical significance of variations between samples The presented software can provide statistical data on a number of aspects or characteristics, such as the Z-score test to detect significant variations in the abundance of species in different experimental conditions; or to contrast the significance of the variation at a species level between samples calculating the p-values. An interesting example is case-control studies in which differences in reads abundance along genomes can be identified. Z-scores provide accurate information on the significance of such differences . This assay is conducted on a particular genome by only considering the reads mapped to annotated regions of the genome and comparing abundance between different samples in the same way as RNA-seq transcriptome expression analysis is performed. Differences in the abundance of reads mapped to annotated regions –when sampling genomic DNA– might be related to environmental changes. This hypothesis is based on the experimental resemblance of the differential expression plot of annotated regions when two samples whose environmental conditions change are compared. Figure Genome profiling of mapped reads A genomic profile of mapped reads is the accumulated number of reads mapping to a given position within the genome. Accumulated histograms of abundance of reads provide information about the number of reads at region level, and therefore about variations in such accumulation (in case-control experiments). In principle, when working with genome sequencing, a more or less flat profile would be expected, as opposed to transcriptomics sequence data. The genomic profile helps detect highly active regions or different number of copies in such regions. This visualization tool  No annotations, in the sense that the annotation files obtained did not contain any annotation at the position where the read was mapped; (2) Semi-annotations, when a part of the mapped read contains annotations, and (3) Full annotations, when the whole read contains an annotation.These three groups are plotted onto the whole mapped metagenome distribution see Fig. d. The bIn order to prove that the results of the proposed workflow are consistent with those of other metagenomic analysis software suites (in terms of abundance in the taxonomic classification), the following test was performed using results from BLASTn based on metagenomic samples from faecal microbial communities. Both, our workflow (MG workflow) and MEGAN were executed using the same input from BLASTn and ran with default parameters , a significant amount of reads would map to unannotated regions, and therefore they would not match to databases composed of genes or proteins.Providing different mapping alternatives helps set up a sort of quality measures of the mapping process based on abundance differences across mapping alternatives. In addition, the study of the different alternatives could reveal hidden interactions or shared similarities between species that cooperate in some aspects.The proposed software is designed to provide evidence of the presence of low-abundance species by finding particular specific regions of genomes with mapped reads. These mapped reads provide strong evidence of the species present in samples. The methods developed for assessing and evaluating the quality of mapping also improve accuracy and reliability in terms of the identification of the species present in a sample.From our perspective, the most important contribution of this workflow software is that it offers the possibility of incorporating new software to extend the analysis workflow by showing datafile specifications enabling fine-grained metagenomic data analysis.Additional file 1Supplementary material. (PDF 1269 kb)


Central vein stenosis/occlusion is a common well-described sequel to the placement of hemodialysis catheters in the central venous system. The precise mechanisms by which central vein stenosis occurs are not well known. Current concepts in central vein stenosis pathophysiology focus on the response to vessel injury model, emphasizing the process of trauma. A case of left brachiocephalic vein stenosis due to the insertion and function of a temporary right subclavian hemodialysis catheter is presented. The purpose of the manuscript is to emphasize that, with the introduction of a temporary subclavian hemodialysis catheter via the right subclavian vein apart from causing concurrent stenosis/infarction of the right subclavian and right brachiocephalic vein, it is also possible to cause stenosis of the left brachiocephalic vein (close to its contribution to the superior vena cava) although the catheter tip is placed in the correct anatomical position in the superior vena cava.  Central vein stenosis/occlusion is a common well-described sequel to the placement of hemodialysis catheters in the central venous system , 2. ThisA 53-year-old man with end stage renal disease, due to renovascular hypertension, started dialysis with right subclavian temporal dialysis catheter four years ago. The introduction of the temporary right subclavian hemodialysis catheter, in the first hospital where the patient was managed, took place with chest X-ray confirmation of the correct catheter tip position in the superior vena cava. A natural radial-cephalic arteriovenous vascular access was created 3 months after that. The performed vascular access needed 6 weeks for its maturation, so the temporal hemodialysis catheter was removed 4.5 months after its insertion. The arteriovenous vascular access was in function for 7 months. The vascular access thrombosis was managed by another ipsilateral proximally brachiocephalic arteriovenous anastomosis which was used directly, due to already maturation of the cephalic vein. In the laboratory investigation, the patient was found to be in procoagulant state with heterozygous factor V (Leiden). A gradually aggravated left upper arm swelling followed by left chest wall swelling and collateral veins dilatation was manifested .The vascular access angiography showed a large stenosis of the left brachiocephalic vein close to its contribution to the superior vena cava , obviousAfter that, a right natural radial-cephalic arteriovenous anastomosis was performed and after its maturation the left arm vascular access was ligated. The new vascular access was in use for 16 months, with incidents of venous thrombotic events, progressive right upper arm edema, and venous hypertension (right subclavian vein stenosis). Patient did not show any signs or symptoms related to cerebral oedema and/or brain disorders.We moved on peritoneal dialysis to manage our patient .The precise mechanisms by which central vein stenosis occurs are not well known. Current concepts in central vein stenosis pathophysiology focus on the response to vessel injury model, emphasizing the process of trauma , catheteThe use of silicone catheters for subclavian cannulation is safe and effective to provide temporary vascular access for acute hemodialysis. The incidence of subclavian vein stenosis due to the use of silicone catheters is lower compared to polytetrafluoroethylene and polyurethane catheters. In a comparative study, the polyurethane catheters were also found to be less traumatic than the polytetrafluoroethylene catheters for the venous intima .The initial stenosis is located at the point of the venous wall trauma caused by the insertion of a venous catheter. Furthermore, the direct contact of the catheter with the venous endothelium into the venous lumen and the venous intima trauma caused by the constant venous movement associated with the procedures of breathing and heart function are causes of venous stenosis. The above causes may lead to generation of thrombin, platelet activation, expression of P-selectin, and an inflammatory reaction with increased inflammatory markers . In an aInflammation has an important role in central venous stenosis. The pathology associated with this lesion is characterized by neointimal hyperplasia, which is a common response closely related to vessel injury and inflammation , 8. In oAnother mechanism of vascular wall damage is the blood flow turbulence at the catheter's insertion site due to constriction of the vein lumen as well as the turbulence of the blood inflow to the venous blood system from the catheter's tip during the hemodialysis . In our Moreover, the hypothesis of uremic environment effect has been supported by recent findings of intimal changes in the cephalic vein of renal failure patients, even prior to arteriovenous fistula (AVF) creation . IntravaIn the 1980s the subclavian venous access was already in a wide use, and a link between subclavian venous catheterization and central venous stenosis was identified back then. In the early 1990s, a removal of the subclavian to the jugular vein catheterization was observed and this option withstood time. Unfortunately, even nowadays, more than 30 years after, the subclavian catheterization continues to be used, even for temporal hemodialysis catheter insertion. Regarding the right subclavian vein catheterization, the venous anatomy should be taken into consideration, as there are two angles in the catheter route: the subclavian to internal jugular junction and the contribution of two brachiocephalic veins to form the superior vena cava. The particularity of this anatomy resulted, in our case, in the left brachiocephalic vein stenosis by a temporal right subclavian hemodialysis catheter. We must note that the initial right subclavian vein catheterization was the cause of all problems that occurred and of such a short time (less than 3 years) of possibility for the patient's therapy by hemodialysis.A temporal right subclavian hemodialysis catheter can cause stenosis, not only to the ipsilateral, but also to the heterolateral brachiocephalic vein, with devastating consequences for the creation and function of a hemodialysis arteriovenous vascular access in both arms, as well as for the arm function.


Identifying perturbed pathways in a given condition is crucial in understanding biological phenomena. In addition to identifying perturbed pathways individually, pathway analysis should consider interactions among pathways. Currently available pathway interaction prediction methods are based on the existence of overlapping genes between pathways, protein-protein interaction (PPI) or functional similarities. However, these approaches just consider the pathways as a set of genes, thus they do not take account of topological features. In addition, most of the existing approaches do not handle the explicit gene expression quantity information that is routinely measured by RNA-sequecing.α. Our approach successfully identified the pathways reported in the original papers. For the pathways that are not directly mentioned in the original papers, we were able to find evidences of pathway interactions by the literature search. Our method outperformed two existing approaches, overlapping gene-based approach (OGB) and protein-protein interaction-based approach (PB), in experiments with the three data sets.To overcome these technical issues, we developed a new pathway interaction network construction method using PPI, closeness centrality and shortest paths. We tested our approach on three different high-throughput RNA-seq data sets: pregnant mice data to reveal the role of serotonin on beta cell mass, bone-metastatic breast cancer data and autoimmune thyroiditis data to study the role of IFN- http://biohealth.snu.ac.kr/software/PINTnet/.Our results show that PINTnet successfully identified condition-specific perturbed pathways and the interactions between the pathways. We believe that our method will be very useful in characterizing biological mechanisms at the pathway level. PINTnet is available at  Identifying perturbed pathways in a given condition is crucial in understanding biological phenomena. Over-representation analysis (ORA) , gene seThere are several approaches to find pathway interactions and construct a pathway interaction network. The first and simplest one is to consider the shared components such as genes or proteins between pathways. It assumes that shared genes may mediate interactions and predicts such interactions by testing the significance of the overlapping genes between pathways using hypergeometric test such as Fisher’s exact test . The secThough the previously mentioned methods suggested an insight on how to predict interactions between pathways, the methods are based mainly on testing differential gene expression. None of these methods use the explicit quantity of gene expression. Therefore, the methods are not able to identify the subtle but important changes in gene expression. Moreover, many of the methods do not take into account the topological features, treating the pathways just as a set of genes. Recently, a path-based approach was studied  but it dpathway interaction network construction method (PINTnet). The summary of our method are: The interactions between pathways are represented by the subnetworks that are constructed considering two topological features: closeness centrality and shortest path.Shortest paths on the subnetworks are computed based on an assumption that pathway interactions occur by a series of spontaneous reactions among genes belonging to the pathways.The explicit quantity of gene expression is used to measure the activation status of pathway interactions.The flow of the changes in expression is weighted. Higher weight is given to any edge between genes when the edge connects differentially expressed genes (DEGs).Here, we propose a new In this section, we describe the process of how PINTnet measures the activation status of the interactions and constructs the pathway interaction network including the preprocess steps in detail. The overview of the method is depicted in Fig. We collected protein-protein interaction data from STRING (ver.9.1)  and pathDefining edges between two pathways is the key issue in constructing a pathway interaction network. Below are the steps for defining edges.We constructed a subnetwork for every possible pair of pathways. To do so, we used two criteria for the pathways to be paired: whether the two pathways have at least one overlapping gene and whether the two pathways have at least one gene connected to the overlapping genes via PPI. We defined every pair of two pathways as a possible pair only when the two pathways satisfied the both criteria. Then, for every possible pair, a subnetwork was constructed using PPI involving the two pathways.For each subnetwork generated above, we calculated closeness centrality of all the genes within. The centrality was to evaluate the degree of a node to be central in a given network, by taking a reciprocal of an average shortest path length to all the nodes within a network from the source. The shorter the average shortest path length of a node, the closer to 1 the closeness centrality of the corresponding node is, otherwise, closer to zero. In this way, genes reflect the topological importance of themselves concerning all possible neighbor nodes within a given subnetwork.A and B, let the genes in A as Agenes={a1,a2,…,am} and the genes in B as Bgenes={b1,b2,…,bn} where m is the number of genes in A and n is the number of genes in B. The shortest paths were computed for every pair of genes ai and bj where 1≤i≤m, 1≤j≤n and both ai and bj are the direct neighbor genes to the overlapping genes. The shortest paths must pass through any overlapping gene of the two pathways.After calculating the closeness centrality, we pruned the genes that are not direct neighbors to overlapping genes in subnetworks. Then, we computed the shortest paths. Given two pathways Finally, we weaved the shortest paths and constructed shortest path-weaved subnetworks (SSN). We conjectured that the pathway interaction occurs by the rapid and spontaneous flow of biological signal or interaction through topologically important genes. This concept is realized in our method by computing shortest path in the weighted subnetworks in terms of closeness centrality. The SSN thus is the network that connects the topologically important genes using the shortest paths. The overview of these steps is depicted in Fig. Measuring the activation status of biological systems or networks is technically difficult. For example, we may want to compute the average expression level of all genes in a network as the activation status of the network. However, this computation completely ignores topological features. A recent work  demonstrk is the index of SSN constructed from each pathway pair, l is the index of an edge, vl1 and vl2 are two genes connected by the edge l, ck(v) is the closeness centrality of a gene v in SSNk and x(v) is the expression level of a gene v. ek,l is the condition-specific edge centrality of edge l in SSNk. After measuring the co-expression score for every edge in SSNk, PINTnet takes the average of the summation of the scores as the activation score (AS) and that is: where N is the total number of edges in SSNk and l is the index of an edge. PINTnet then calculates the ratio of where Computing DEGs is a simple but effective approach for detecting perturbed pathways and even signaling impacts in the pathways in a given condition. However, DEGs are widely interspersed and are not connected in the networks or pathways. To utilize DEG information, we applied the ratio of DEGs that are connected by edges as a weight. A higher interaction score is assigned for more DEG connections. In this step, PINTnet simply calculates the fold change of expression level of each gene to define DEGs and the default threshold is log20.5 as used in other studies that used RNA-seq data –21. The k is the index of SSN, ISk is the interaction score of SSNk, SSNk of the case data, SSNk of the control data, U is the number of connected up-regulated DEGs of the case data compared to the control data and D is the number of connected down-regulated DEGs of the case data compared to the control data. When PINTnet calculates the fold change, the cutoff value of 1.0 for the expression level is used to prevent noise such as extremely high fold change due to the comparison between small numbers. The cutoff value was set based on other studies [where  studies , 23. In  studies . We trieAfter measuring the activation status of all pairs of pathways and obtaining the interaction score, PINTnet converts the interaction score using the sigmoid function . It is tAfter the interaction score is converted, a pathway interaction network is constructed with the edges between pathways when the interaction score of edges satisfies the cutoff value. We empirically determined the cutoff value by testing PINTnet on various data from other biological researches.To evaluate the performance of PINTnet, we used three different high-throughput RNA-seq datasets in Gene Expression Omnibus (GEO). Cytoscape was used to visualize the networks . The tesDataset1 is the data that measured the gene expression levels of pregnant mice to reveal how serotonin regulates pancreatic beta cell mass during pregnancy [Tph1 and Tph2 were the genes most markedly induced during pregnancy. These two genes encode two isoforms of tryptophan hydroxylase, the rate-limiting enzyme in the synthesis of serotonin, 5-HT. The authors also reported that beta cells share a common gene expression program and the ability to synthesize, store and secrete serotonin with serotonergic neurons.regnancy . The autDataset2 is the data generated by a study investigating how ABL kinases promote breast cancer osteolytic metastasis [tastasis . Bone isDataset3 is from a study that used thyroiditis as a model to reveal how IFN- α plays a pivotal role in auto immunity [α in the thyroid and performed RNA-seq analysis. The transgenic mice showed upregulation of pathways such as antigen presentation pathway, interferon signaling, complement system, apoptosis, pattern recognition receptors and RAR activation.immunity . The autβ-cells. A lack of serotonin in β-cells can lead to reduced insulin secretion [It is well known that nutrient requirements by the fetus incur change in the maternal metabolism during pregnancy. Nutrient flow to the fetus is maintained by increasing insulin resistance. The resistance may cause maternal hyperglycemia but the glucose level is maintained by the expansion of beta cells driven by prolactin and placental lactogen –32. Failecretion . Also, iecretion –37 and iecretion .ABL1 and ABL2 belong to and it is known that Ras signaling pathway activation is implicated in breast cancer invasion and growth [β signaling pathway was reported to be important for the development of osteolytic bone metastases by numerous studies [Ras signaling pathway is the pathway which d growth . Thus thd growth . MAPK sid growth . In addid growth , 43. Wntd growth . Upreguld growth , 46. TGF studies . Proteog studies . A high  studies . Hippo s studies . In addi studies –59.It is known that Toll-like receptor signaling pathway plays an important role in autoimmunity including thyroid autoimmunity , 61. AlsTo compare the performance of PINTnet to other approaches, we implemented the overlapping gene-based approach (OGB) and the PPI-based approach (PB) and ran three methods including ours on the three test datasets in the previous sections.p-value of 0.05 or less. The significant edges were used to construct a pathway network.This method is a two-step approach. In the first step, the activation status of each pathway was calculated using Fisher’s exact test with a contingency table dealing with two parameters: one is whether a gene is a DEG or not and the other is whether a gene belongs to the pathway or not. Then, in the second step, the significance of edges among pathways was evaluated using Fisher’s exact test. The significance of pathways and the edges among the pathways were determined at a p-values for every possible pair of pathways to find significantly interacting pathway pairs by shuffling the original PPI network 1,000 times, counting the number of round when the number of shuffled PPI edges between pathways was bigger than or equal to that of the original PPI edges and dividing the number by the number of round, in this case, 1,000. Then we adjusted the p-values using Bonferroni correction and took the edges with the p-value less than 0.05 as the significantly interacting edges. Connecting the edges, we constructed a template network and calculated active PPIs on the network using the datasets.For PPI-based approach, we implemented the simple version of the approach since no executable code is available. We implemented the approach based on the hypothesis that the more interactions may guarantee the higher probability of interaction. To do this, we calculated the empirical dataset1. It seemed almost impossible to determine which pathways and interactions between the pathways were important in the given conditions. Thus we did not include this approach for the performance comparison.Running the approach on the test datasets, we observed that too many nodes and edges were connected even though multiple testing correction was performed using the Bonferroni correction. For example, there were 271 nodes and 12,264 edges for We compared the performance of the approaches based on the biological evidences found by the literature search and organized in Evaluation criteria section. The following criteria were used for the quantitative measure of the performance. The first criterion was the interactions between the pathways. We calculated the percentage of the number of the evidence-supported edges between the evidence-supported pathways against the total number of edges in the network. It was to measure how successfully the approaches connected the correct edges supported by evidence. The second criterion was the degree of pathways. We calculated the ratio of the average degree of the evidence-supported pathways against that of all pathways in the network. It was to measure how the approaches placed the important pathways as hubs in the central position of the network. The last criterion was to see how successfully the approaches rescued the correct pathways. We calculated the percentage that how many evidence-supported pathways were rescued. We confirmed that PINTnet surpasses other methods from the results described in the following paragraphs and the comparison results are shown in Table dataset1 with the cutoff value of 0.95, there were 60 pathways and 92 edges between the pathways. The network is shown in Fig. In the pathway interaction network constructed using our method on dataset2 with the cutoff value of 0.99. The network is shown in Fig. β signaling pathway (hsa04350), PI3K-Akt signaling pathway (hsa04151) and proteoglycans in cancer (hsa05205). The result implies that the pathways implicated in bone metastasis from breast cancer interact each other and the interactions among the pathways along with the paths may give the insight of how bone metastatic breast cancer is caused by pathways interaction. However, TGF- β signaling pathway was not rescued by OGB and only two edges were detected: MAPK signaling pathway and proteoglycans in cancer; Ras signaling pathway and PI3K-Akt signaling pathway.There were 66 pathways and 122 edges between the pathways in the pathway interaction network constructed using our method on dataset3 with the cutoff value 0f 0.99 included 102 pathways and 149 edges between the pathways. The network is shown in Fig. α belongs to and autoimmune thyroid disease is the overall context of the original paper. Moreover, the findings reported in [The pathway interaction network constructed using our method on orted in , 61 suppp-value yielded by the tests or use the p-value itself to calculate the secondary score for the determination. In addition, even though several approaches incorporated PPI to infer the interactions between pathways, the approaches have a limitation that PPI was treated merely as a set of individually represented genes without considering any relation between the genes. To address these issues, we applied the concept of closeness centrality and shortest paths to define the edges in the pathway interaction network. We assumed that the interaction between two pathways will occur when the biological signals rapidly flow through the topologically important genes. Based on the assumption, we constructed shortest path-weaved subnetworks to represent the edges and calculated interaction score using explicit gene expression quantity on the subnetworks.Currently available approaches for constructing pathway network were designed to handle microarray data so the approaches mostly rely on the statistical tests. The approaches determine the significance of the interaction by the The scoring scheme of PINTnet is a ranking method. It constructs the pathway interaction network using pairs of pathways of which the score is higher than the cutoff value. The results on the test datasets suggest that PINTnet successfully reproduced the results of the original papers and, therefore, is useful in analyzing the perturbed pathways and their interactions in a given condition.Like existing methods, PINTnet is based on the identification of overlapping genes between two pathways. We assumed that the overlapping genes function as a bridge between two pathways. Based on the assumption, we considered the situation that at least one overlapping gene exists as one of the rules to define the edge in the pathway interaction network. This criterion, though reasonable and popular, may be too stringent. For example, when two pathways are well connected by direct edges in PPI but do not share any genes, it is not clear whether the two pathways interact or not. Therefore, the pairs of truly interacting pathways might be ruled out. We will further work on the matter to overcome the limitation.dataset1, PINTnet successfully detected the pathways related to the changes occurring during pregnancy. Also we observed that the pathways were connected by the edges supported by the literatures. For dataset2, we also identified that the pathways related to bone-metastatic breast cancer were rescued in the pathway interaction network and the edges between the pathways implied the interactions participating in the induction of the phenotype. For dataset3, the pathways reported by the original paper were included as nodes in the pathway interaction network and there was a connected edge between Toll-like receptor signaling pathway and autoimmune thyroid disease. We expect PINTnet to be a useful tool for pathway interaction network analysis. PINTnet is available at http://biohealth.snu.ac.kr/software/PINTnet/.In this work, we developed a new pathway interaction network construction method, PINTnet. Running PINTnet on the three datasets to test the performance, we observed that it successfully rescued the findings reported in the original papers. In the result of