The os peroneum (OP) is a small sesamoid bone located inside the peroneus longus tendon (PLT), close to the cuboid. The OP can be the cause of pain and can be associated with lesions of the PLT. OP involvement in PLT disorders is frequently misdiagnosed by radiologists. Painful os peroneum syndrome (POPS) refers to a variety of conditions presenting with pain localized on the lateral aspect of the cuboid area. The syndrome can be observed as a consequence of local acute trauma such as ankle sprains or chronic overuse. Because of its intra-tendinous location, in tears of the peroneus longus tendon, the OP can show changes in its morphology or position, depending on the location of the tendon’s tear. Based on the level of the PLT tears, we propose a classification in three subtypes: tears localized proximal to the os peroneum (type I), at its level (type II) or distal to it (type III). These tears present with different changes on OP morphology or location. The aim of this article is to review the normal anatomy, imaging appearance and differential diagnosis of disorders of the OP as well as post-treatment imaging.Teaching points• PLT tears can be classified in three subtypes according to OP location.• POPS is characterized by pain on the lateral aspect of the cuboid.• OP involvement in PLT disorders is frequently misdiagnosed by radiologists. Disorders of the peroneal tendons (PT) are frequent in the everyday practice of rheumatologists, orthopaedics, sports medicine doctors and physiotherapists –4. The oThe PT complex is made up of the muscles and tendons of the PLT and peroneus brevis tendon (PBT), their common synovial sheath, the superior and inferior retinaculum, and the OP , 11–15.The peroneal longus (PLM) and brevis (PBM) muscles are located inside the external compartment of the leg, lateral to the fibula. The PLM originates from the proximal portion of the lateral surface of the fibula and from the nearby intermuscular septum. The PBM originates from the distal third of the lateral surface of the fibula and from the nearby intermuscular septum. At the level of the leg’s median third, the PLM extends through a superficial, flat aponeurosis that covers the PBM. The PBM presents a myoaponeurotic junction distally located with regard to that of the PLM. Both muscles are innervated by the peroneal superficial nerve. The peroneal muscles are supplied by two principal source arteries, the anterior tibial artery and the peroneal artery.At the malleolar region, the two tendons reflect against the posterior aspect of the lateral malleolus. At this level they run inside an osteofibrous tunnel  made up of the concave bony gutter and the thick fibrous superior retinaculum. This inserts into the lateral surface of the malleolus and guarantees the stability of the tendons during ankle movement, thus preventing their anterior luxation during movement of the ankle.Distal to the lateral malleolus, the PT travel along the lateral surface of the calcaneus, which presents a small bony prominence, the peroneal tubercle (PTub). The peroneus brevis tendon (PBT) runs superiorly while the PLT runs inferiorly to it. At this level, the two tendons run inside another osteofibrous tunnel , stabilized against the calcaneus thanks to the inferior retinaculum, which inserts on the apex of the PTub. The tubercle acts as a pulley for the PLT that reflects on its inferior surface.Distal to the PTub, the PBT travels straight forward, to insert into the base of the fifth metatarsal bone.The PLT, after reflecting against the cuboid, reaches the plantar region and attaches to the first and second metatarsal bones.The two PT are enclosed by a common synovial sheath allowing optimal sliding of the tendons during movements of the ankle and/or contraction of the peroneal muscles. The sheath is made of a parietal layer adherent to the paratendinous soft tissue and a visceral layer adherent to the tendons. Proximal to the PTub the sheath divides to individually surround the two tendons. In some cases, the sheath surrounding the PLT is longer and extends from the cuboid tunnel to the plantar aspect of the foot. In physiologic conditions, it contains only a small amount of synovial fluid.The OP is a small sesamoid bone which can be found inside the PLT at the level of the calcaneocuboid joint. The OP is ossified in around 20% of the population, and it is bilateral in around 60% of cases , 16, 17.The presence of an OP or a bipartite OP can be clinically significant. The differential diagnosis between the ossicle and a cortical avulsion or a soft tissue calcification can be difficult in patients with pain in the lateral side of the foot. Similarly, a multipartite bone must be differentiated from a fractured one.The origin of OP development is controversial. Some authors advocate an embryonic development of the ossicle , while oThe OP can be visualized with several imaging techniques.Internal oblique radiographs of the foot can visualize the OP as an oval, well-corticated ossicle located close to the calcaneocuboid joint. The size, margins and location of the OP are easily assessed Fig. . In multUltrasound (US) allows an analysis of the OP when using a high-frequency transducer and a rigorous examination technique Fig.  5, 21]., 21.5, 2Computed tomography (CT) allows a more precise evaluation of the OP, particularly when it is realized on a multidetector scanner with sub-millimetre collimation and oblique reconstruction on several planes Fig. . It is tMagnetic resonance imaging (MRI) allows an accurate evaluation of the OP Fig. . The OP When no proper OP is visible, and only a fibrocartilaginous node is present, it is important to avoid one of the most frequent pitfalls of this situation. On MRI, the fibrocartilaginous node appears as a “pseudo tear”. Due to its chemical composition, it has an intermediate signal and can be mistaken as a real tear by an inexperienced radiologist .Bone scintigraphy can detect the uptake in the area of the OP in chronic lateral foot pain with an unclear clinical history (micro-traumas or mismanaged non-recent traumas). In these cases, the nuclear medicine specialist must bear in mind the possibility that an uptake in this area can be related to a pathology of the OP .POPS refers to a variety of conditions presenting with pain localized on the lateral aspect of the cuboid area or on the OP .The syndrome can be observed as a consequence of local acute trauma such as ankle sprains. In these cases, tears of the PLT or acute OP fractures are typically observed. Chronic overuse can also lead to POPS secondary to bone marrow oedema of the OP and cuboid, OP stress fractures, local impingement or partial tears of the PLT , 26, 27.In acute fractures, standard radiographs show multiple fragments of the OP, typically presenting sharp borders. This must be differentiated from a multipartite OP, in which the fragments are rounded and present regular and sclerotic cortical edges . CorrelaIn the majority of cases, US does not show abnormalities of the OP , which sCT can assess the morphology of the OP well and allows a definite diagnosis of its acute or stress fractures, and in some cases points out erosions of the cuboid cortical bone . This teMRI is the gold standard in diagnosing POPS, since it shows pathologic changes of the bone marrow of the OP and cuboid as well as of soft tissues. Oedema appears hypointense on T1w sequence and hyperintense on T2w sequence with fat suppression Fig. . After gBone scintigraphy can detect the uptake in the area of the OP in chronic lateral foot pain. In these cases, that uptake can be related to a pathology of the OP  Fig. 1010.Fig. 1Because of its intra-tendinous location, in tears of the PLT, the OP shows changes in its morphology or position. Based on a review of the literature , 16, 23,When the PLT tear is proximal to the OP, the ossicle presents a normal appearance and localization. This lesion cannot be diagnosed with radiographs or CT, which show a normal OP, but can be detected with ultrasound and MRI as irregularity of the tendons with local discontinuity usually associated with tenosynovitis.Tears of the PLT at the OP level present as ossicle fractures with two or more bone fragments of different sizes. As noted, OP fractures can be acute or chronic and present as a POPS syndrome. Diabetes mellitus is a factor facilitating fractures of the OP . WhateveStandard lateral and internal oblique radiographs are extremely useful for detecting an OP fracture , and shoUS can easily show a fracture of the OP when a significant displacement (>6 mm) of the posterior fragment is present. In these cases, US shows the bone fragments localized inside the tendon along the lateral side of the calcaneus. The retracted proximal fragment is usually found at the level of the PTub. The distal fragment can be more difficult to detect because of its smaller size. The distance between the two can be accurately measured Fig. . This caCT can detect the presence of the fracture even if minimally displaced, especially if performed with a multidetector scanner. Retraction of the proximal fragment is easily appreciated.MRI is less useful than CT in analysing the bone cortex in non-displaced fractures, but allows the detection of bone marrow oedema and adjacent soft tissue inflammation in fat saturation images. In displaced fractures, MRI can easily detect the position of the retracted fragment and can demonstrate possible impingement of the retracted fragment at the level of the PTub inside the distal tunnel. MRI impingement features are similar to those of classic POPS but located more proximally. We call this lesion the “proximal POPS”. In addition, MRI shows tendon lesions and effusion inside the synovial sheath .When tears of the PLT are located distal to the OP, the sesamoid shows a normal appearance even though it is posteriorly displaced, because of the traction exerted by the peroneus longus , 37. DisThe internal oblique radiograph is the best way to highlight the posterior unusual localization of the OP. On lateral radiographs, this small bone is hard to localize because it is superimposed on the calcaneus. For these reasons, this diagnosis can be challenging for less experienced radiologists and is generally easier when a clinical suspicion is present. A comparison with previous radiographs is extremely important because it can confirm the displacement of the OP  , 34, 39.US easily confirms type 3 tears. The degree of the displacement and the size of the proximal tendinous stump are easily measured. This can aid preoperative planning in order to correctly plan surgical incision points. Lastly, the alterations of the tendon sheaths are visible with US: effusion, sheath thickening and hyperaemia.CT shows a regular appearance of the displaced sesamoid bone. The main differential diagnosis is with an accessory os subfibulare. Unfortunately, the tendon lesion is not evident in most cases.This type of lesion can be identified with MRI, especially after contrast medium injection. Coronal images easily show the proximal stump, the displaced OP and the empty tendon sheaths distally located.Treatments for OP-related pathologies are extremely varied, ranging from non-operative treatments even in high-level athletes , to repaTo our knowledge, no studies are available on postoperative imaging of the PLT and OP. Hypothetically, imaging can detect acute complications such as infection or re-rupture and late complications such as reflex sympathetic dystrophy syndrome (RSDS) or neuromas .The actual prevalence of the OP pathology is difficult to assess, but in our opinion, it is probably underestimated. Pathologic involvement of OP is frequently misdiagnosed by radiologists.When an OP fracture is suspected, standard radiographs allow for a correct diagnosis only if carefully evaluated. In case of doubt, the contralateral ankle or previous radiographs must be used for comparison.MRI is an extremely powerful tool but needs proper plane and field of view selection. It is the gold standard for the detection of POPS.When a PLT tear is suspected, US can be used as a first approach. Also for US, contralateral ankle evaluation is extremely useful.


Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%. Concrete is one of the most widely used materials for civil infrastructure such as bridges, buildings, and nuclear power plants because of its cost-effectiveness and convenience for molding into desired shapes. However, concrete structures inevitably suffer from cracks caused by creep, shrinkage, and loading that potentially degrade structural soundness. Indeed, cracks are an important indicator of structural health; consequently, crack monitoring is considered an essential maintenance process for civil infrastructure.In general, cracks on concrete structures are inspected by manual visual observation of the surface. The observed information, including the length, direction, and width of the cracks are tracked over time to evaluate the current condition of the structure and anticipate crack growth, which is used to assist with maintenance plans. Although manual visual inspection is the most common practice applied to monitor concrete cracks, this method is intrinsically inefficient as it is time-consuming, expensive, and even unsafe for inspectors in the case of inaccessible structural members.Recent advances in unmanned aerial vehicle (UAV) technologies have produced low-cost and high-mobility UAVs, rapidly broadening their real-world civil engineering application ,4,5,6,7.Various image processing techniques have been implemented for effectively extracting the crack information from images. For example, edge detection has been utilized to provide a boundary between a crack and its background ,24,25 anImage binarization is known to be one of the most commonly used image processing methods. In the binarization process, crack objects are identified by categorizing the pixels whose values are less than a specified threshold into black. Thus, the binary objects considered cracks can be used to determine the crack width and length with an associated working distance. However, although image binarization is useful for separating cracks and backgrounds, the crack assessment is difficult to standardize due to a high dependence of binarization on the parameters determined by users. Kim et al.  conducteThis paper presents a hybrid image processing-based crack identification using UAV. The UAV-based system designed in this study is capable of image and distance sensing, as well as wireless communication, which makes it possible to control sensing and data transmission while the UAV is in the air. The obtained information is subsequently processed by the proposed hybrid image processing to identify crack width accurately while minimizing the loss of crack length. The performance of the crack identification strategy using UAV is experimentally validated using a concrete wall with various types of cracks.The first step in crack width calculation based on image binarization is to convert the RGB image to grayscale. Subsequently, a threshold is calculated using the statistical properties of the grayscale values  to categorize each grayscale pixel into black and white. For example, as shown in m is the mean in each selected window, s is the standard deviation, R is the dynamic range for normalizing s, and k is the sensitivity to control the contribution of the statistical parameters. Since the thresholds highly depend on two user-defined parameters of window size and sensitivity, these parameters should be appropriately selected for accurate binarization results.A wide variety of image binarization methods have been developed primarily for text detection from images ,35,36,37rW is the real crack width in metric (mm), pD is the resolution of the imaging system, pW is the obtained crack width in pixel, wD is the working distance in mm, cP is the pixels per centimeter of the used camera sensor, and fL is the focal length of the camera in mm.The binary images are subsequently processed by skeleton and edge detection and crack width calculation. Each group of connected black pixels, representing a crack segment, is decomposed into a skeleton and edges using the thinning method  and edgeImage binarization has to be cautiously applied to crack assessment because of its dependence on user-defined parameters. Image binarization is capable of effectively extracting crack information using a simple equation, such as Equation (1); however, the measurement accuracy of the detected crack width and length is generally affected by the image binarization parameters initially provided by the user. For example, as shown in The UAV-based prototype used in this study is designed to effectively acquire necessary data for crack identification . The proRaspberry Pi B+, a low-cost low-power single-board computer running Linux, is utilized to control sensing and communication. The Raspberry Pi is interfaced with the camera, the displacement sensor, and the USB WiFi module. The Raspberry Pi B+ takes images using the camera, measures the working distance between the camera and the concrete structure, and is controlled by and sends data to a remote computer using the WiFi module. The USB WiFi module mounted on the Raspberry Pi provides wireless connection between the UAV-based system and the operator’s computer through a WiFi router. Remote access to the Raspberry Pi of the UAV-based system allows operators to acquire image and distance information when desired and to wirelessly transmit the acquired data. The operator can monitor the video being taken by the camera, and instantly acquire image and distance data that are wirelessly transmitted to the operator’s computer.The camera module LS-20150) and the ultrasonic displacement sensor (HC-SR04) provide crack images and the corresponding working distances, which are required to determine crack sizes. In previous studies ,20,21,22150 and tAll the components of the proposed system are selected to be low-cost and lightweight. The total weight of the sensing and communication components  is approximately 60 g, which does not significantly affect the flight of the UAV. To further reduce the weight, the sensing and communication components are designed to share the UAV’s battery. The power consumption of the Raspberry Pi is approximately 2 W, which is significantly less than that of the UAV (70 W).In addition to the hardware described in Section III, an image processing strategy tailored to the UAV-based system is developed in this study. The main idea underlying the strategy is to consider a combination of different sets of binarization parameters for accurately extracting the crack width while minimizing loss of length. The proposed hybrid image processing strategy comprises two stages: (1) image pre-processing to prepare the image for further analysis and (2) crack width estimation using the hybrid approach.The image pre-processing stage consists of two steps: (1) image undistortion and (2) conversion from color image to grayscale image. As shown in wP and lP designate these two sets:wP: optimal parameters minimizing estimation errors in crack width; andlP: optimal parameters minimizing estimation errors in crack length.Hybrid image processing is applied to the pre-processed image to determine crack width and length accurately. As stated in wP and lP are then separately employed to generate two binary images using Sauvola’s method. lP inevitably results in a higher threshold than that of wP to convert more pixels to crack elements.wS: set of skeleton pixels obtained using wP;lS: set of skeleton pixels obtained using lP; andw: crack width at location S obtained using P.where P is either wP or lP, and S is a set of skeleton pixels. Selecting wP to produce the accurate crack width of w results in more unidentified crack elements than lP. Thus, wS is a subset of lS, because the obtained thresholds of lP are greater than those of wP. However, the calculated widths obtained using lP ) are overestimated owing to the high thresholds.The binary images are subsequently processed using the steps for skeleton and edge detection and crack width calculation, as described in w and w, which are the crack widths using wP at wS and lP at l − SwS, respectively. The overestimated crack width w has to be corrected, which enables the simultaneous generation of accurate crack width and length. The calibration for the overestimated w can be performed by utilizing the ratio of w and w, as defined in Equation (3):α is the calibration factor, N is the number of skeleton pixels in wS, and iw is the crack width at the ith skeleton pixel. The calibration factor α is then multiplied by w to correct the overestimation. The crack widths of w and αw are combined to provide complete width and length information in lS. The overall procedure of the proposed hybrid image processing strategy is summarized in The final crack widths are a combination of wP and lP. Subsequently, crack identification by hybrid image processing was performed using crack images obtained from the UAV-based system.A field testing is conducted to demonstrate the validity of the proposed hybrid image processing in conjunction with the UAV. First, parametric analysis was conducted to determine the two sets of optimal parameters, In the parametric analysis, 20 crack images with different surface textures, crack widths, lengths, directions, and sizes, were prepared to address various concrete conditions. The collected image pool was processed using Sauvola’s method with a wide range of binarization parameters. Then, crack width and length information was obtained using the procedure presented in w, for optimal width, is defined as:pN is the total number of images in the pool, tN is the number of color targets, ew is the estimated crack width from Sauvola’s method, and mw is the width measured by the optical microscope. The second cost function Jl, for optimal length, is defined as:el is the estimated crack length from Sauvola’s method, and tl is the total length verified visually in the grayscale image. dC and tC are the numbers of detected crack and total pixels in the entire binary image, respectively. The second term in Equation (5) prevents all the pixels from being converted into cracks.Two cost functions were defined to determine the optimal parameters in terms of crack width and length. The first cost function JThe cost function values of each crack information set  are analyzed with respect to the binarization parameters, as shown in Field testing was conducted on a concrete wall in the gymnasium building on the UNIST campus see . The conwP can also be detected and calibrated accurately.The crack identification using the hybrid image processing is applied to the captured images. To validate the performance of the proposed hybrid method, the binarization results were compared with the results of Sauvola’s method with default parameters adopted from . As showCracks thinner than about 0.1 mm, which can be seen with the naked eye, are not found even with the hybrid image processing as shown in The minimum detectable crack size is reported as the most important parameter of the inspection method . From thWhen a UAV is used with computer vision, parallax during image acquisition is an important issue . As thiswP and lP, to accurately detect crack widths while minimizing loss of crack lengths. The results of the experimental evaluation can be summarized as follows:(1)While the crack widths less than 0.25 mm were typically unidentified or underestimated in case of the default values, the proposed hybrid method measured all ranges of crack widths reliably.(2)The maximum length estimation errors were 7.3% and 52.3% for the hybrid method and Sauvola’s binarization with the default parameters, respectively, proving significant performance improvement by the hybrid method.This paper presented the crack identification using the hybrid image processing strategy with a UAV. A prototype of the UAV-based system was built using a Raspberry Pi connected to a camera, an ultrasonic displacement sensor, and a WiFi module. The Raspberry Pi controlled sensing and wireless communication, providing crack images with associated distances on demand. The obtained information was subsequently processed by the hybrid image processing method using two sets of optimal parameters Consequently, the results of experimental evaluation on a concrete wall show that the proposed UAV and hybrid image processing-based crack identification strategy effectively and reliably identifies cracks.


As NOx emissions legislation for Diesel-engines is becoming more stringent than ever before, an aftertreatment system has been widely used in many countries. Specifically, to reduce the NOx emissions, a selective catalytic reduction(SCR) system has become one of the most promising techniques for Diesel-engine vehicle applications. In the SCR system, input ammonia concentration and ammonia coverage ratio are regarded as essential states in the control-oriental model. Currently, an ammonia sensor placed before the SCR Can is a good strategy for the input ammonia concentration value. However, physical sensor would increase the SCR system cost and the ammonia coverage ratio information cannot be directly measured by physical sensor. Aiming to tackle this problem, an observer based on particle filter(PF) is investigated to estimate the input ammonia concentration and ammonia coverage ratio. Simulation results through the experimentally-validated full vehicle simulator cX-Emission show that the performance of observer based on PF is outstanding, and the estimation error is very small. Among all types of aftertreatment systems, pointed in Ref. [In recent years, a phenomenon is that Diesel-engines have got more attentions due to their excellent fuel efficiency and lower greenhouse emissions compared with gasoline counterparts. And Die in Ref. , the ureIn the urea-based SCR system, the primary principle is to spray urea solution into the exhaust pipe before the SCR tank. Under the high temperature exhaust condition, the injected urea solution can be converted into gaseous ammonia which is then absorbed on the catalyst surface and reacts with NOx. GeneralAmong the SCR closed-loop control strategies, the input ammonia concentration and the ammonia coverage ratio are considered as the critical states in the construction of the feedback control strategy, 14. To In this paper, we propose a technique by using the PF to simultaneously estimate the ammonia input and ammonia coverage ratio. As a contrast, an observer based on the EKF is also designed. The contributions and novelty of the work can be summarized as: (1) Considering the strong nonlinearity, it aims to propose the PF algorithm for the SCR system. (2) Based on the PF, the observer is designed to estimate the ammonia coverage ratio which is a crucial state and cannot be directly measured by physical sensors, and the input ammonia concentration is estimated such that the urea-to-ammonia rate is available without mounting an extra ammonia sensor at the entrance of the SCR tank. (3) The proposed PF observer is verified by using the data from an experimentally-validated full vehicle simulator cX-Emission. The comparisons show the advantage of the proposed PF observer over the traditional EKF observer.The rest of this paper is organized as follows. A concise introduction of the SCR reaction model is shown in section 2. Then an observer model for state estimation is designed in section 3. In section 4, the simulation results are presented to verify the performance of the proposed technique. At last, concluding remarks are summarized in section 5.The main function of a SCR system is to catalytically convert the engine-out NOx into nitrogen and water, and consequently, reduce the NOx emissions in the exhaust tailpipe of a Diesel-engine. Therefore, in the urea-based SCR system, the 32.5% aqueous urea solution is injected into the upstream exhaust pipe. The schematic diagram of a SCR system is shown in The chief chemical reactions and corresponding reaction rate equations are summarized as follows:(1)Urea evaporation: the urea solution evaporates into solid phase of urea under the ideal situation. The reaction is represented as follows:where (2)Urea decomposition: solid urea is decomposed into ammonia and equimolar amount of isocyanic acid when the exhaust gas temperature is sufficiently high. The reaction is shown as follows:(3)Isocyanic acid hydrolyzation: in this part, the isocyanic acid is hydrolyzed to ammonia and carbon dioxide. The reaction is described as follows:(4)Adsorption/desorption: the ammonia in the catalytic tank is adsorbed on the SCR substrate, and the adsorbed ammonia can also be desorbed from the substrate. The reversible reaction is represented as follows:free is the free SCR catalyst site, and the 3 adsorbed on the SCR substrate which is ready to react with NOx; the forward direction describes the adsorption process while the reverse direction denotes the desorption process.Where θThe rate of the revisable reaction can be shown as follows:where Rx represents the rate of the chemical reaction; T is temperature; E, K, and R are constants; Cx expresses concentration of x, and where (5)NOx reduction: the chief reduction reactions can be described as follows:Among the Diesel-engine exhaust NOx, NO is more than 90%, and the reaction rate of Eq  is quite(6)3 would be oxidized to NO. The reaction equation can be proposed as follows:Ammonia oxidation: if the temperature is more than 450°C, the adsorbed NHThe reaction rate is presented as below:In this work, we suppose that the SCR reaction device is a Continuously Stirred Tank Reactor (CSTR). Based on the above chemical reactions, molar balance and the conservation of mass, the dynamic equation of SCR system can be established as follows.NO and NO,in indicates the inlet ammonia and NOx concentration respectively. F is the exhaust gas flow rate; V is the volume of the SCR Can.In the literature, there are many SCR models for ground vehicle applications such as the four-state model in, and fivIn appropriate SCR controllers, input concentrations, state information, output concentrations and temperature are necessary. In general, two ammonia sensors, two NOx sensors and one temperature sensor are mounted in the SCR device to measure the essential parameters. The schematic diagram of the SCR system and location of sensors are presented in In order to develop the observer, there are considerable methods. Considering the system nonlinearities and measurement noises, the EKF algorithm is the most classic and widely-used method. However, the method requires that the linearization of the nonlinear model to which the KF can be applied keeps small deviation, the nonlinearity of the model is not big and the noise is Gaussian or independent and identically distributed which are hard to satisfy in the SCR system, 20. TheIn our study, the traditional EKF and PF have been used to estimate the ammonia input concentration and coverage ratio. The algorithms for the two observers are described in the following sections. Then the state space model for observer is presented.xk and uk is the state vector and input vector respectively, f(x) expresses the relevant prediction function, wk denotes the process noise whose covariance is Qk. In (19), h(x) is the measurement function, the corresponding noise and noise covariance are vk and Rk respectively.The EKF algorithm is the most widely used state estimation technique in nonlinear system which uses a linearized model and KF algorithm at each sample time. The nonlinear system can be described as below:Following a linearization procedure of Eq  and Eq , linear Then the EKF recursion can be summarized below.(1)xk and the system error covariance Pk are obtained as follows.Prediction step: the priori estimations of the state vector (2)Calculate the Kalman gain: the Kalman gain defines the extent to which the observer trust the state estimate or measurement.Kk=Pk−H(3)Measurement update step: calculate the posteriori estimations of the state vector and error covariance using the measurement information.Unlike the EKF which requires the linearization of the nonlinear system, the PF algorithm does not require that the system is linear or Gaussian distributed by using the Bayesian filter and Monte Carlo approach. This method begins with a set of random particles which are then propagated through the state transition equation to obtain priori particles. Then, the available measurement information is fused with these priori particles to give posteriori distribution. An excellent tutorials of PF can be seen in . For a n(1)xi0, are generated based on the initial pdf of p(x0).Initialization: a set of random particles (2)qi which can be calculated as follows.Importance sampling: the randomly generated particles are propagated through the state transition equation Eq  to obtaiz* indicates the measurement value.where (3)Resampling: resampling approach are made to reduce the degeneracy phenomena during the iteration. After resampling, the particles with large weight are replicated and particles with small weight are discarded. There aSince the system model is built and the necessary parameters can be measured by physical sensors, the input ammonia concentration and ammonia coverage ratio can be estimated by EKF and PF described above. As the ammonia concentration varies very slowly over time, then, the derivative of input ammonia concentration can be assumed as zero:C˙NH3,iThe state space model in discrete form for input ammonia concentration and ammonia coverage ratio predictions are built as follows:NO,in is the NOx input concentration value measured by physical NOx sensor placed before the SCR Can.The post NOx sensor and ammonia sensor reading are selected for the measurement equation which can be shown as follows:Remarks: In the observer designed, two NOx sensors placed at the entrance of the SCR Can and the tailpipe respectively are needed and one ammonia sensor located at the tailpipe is also necessary. It is worth noting that most onboard NOx sensors have significant ammonia cross sensitivity which would make the observation unsatisfactory if the inaccurate NOx sensor reading is used. However, by using the measurement value of ammonia sensor and temperature sensor in the tailpipe, the NOx sensor reading can be corrected to eliminate the effect of gaseous ammonia species. In recent years, a lot of approaches have been proposed to obtain the corrected NOx sensor[Ox sensor, 37. In The parameters of the SCR system model established above are derived from the previous research results in . With thFor the simulation, as we can see in the SCR system model in Eq , the exh2 concentrations. The model predicted tailpipe NOx and ammonia concentration are presented in −4. Therefore, as expected, we can claim that the proposed estimation method based on PF algorithm is more accurate for estimating the input ammonia concentration and can meet the actual needs.As above-presented, the designed input ammonia concentration and ammonia coverage ratio observer based on PF is simulated based on the selected FTP-75 cycle. As is mentioned above, the ammonia coverage ratio cannot be directly measured by a physical sensor. Thus, we can only compare the ammonia coverage ratio values between the SCR model and the designed observers. And The PF observer for input ammonia concentration and ammonia coverage ratio estimations is studied in this paper. Though the input ammonia concentration value can be measured by ammonia sensors, this method will increase the cost and diagnosis challenge of the SCR device. Inspired by the problem, the PF observer is designed to replace the physical sensors. According to the comparison results, the performance of the designed observer based on PF is excellent during the FTP-75 test cycle. Therefore, with the assistance of the developed observer, the ammonia sensor placed at the entrance of the SCR tank can be cancelled. What’s more, the ammonia coverage ratio information is critical in the construction of the feedback control strategy and cannot be measured directly by physical sensor. And the simulation results and comparisons show that the designed PF observer has a pretty good ability to estimate the ammonia coverage ratio value.In the future research, more experiments for Diesel-engine and SCR system will be done gradually, and other kinds of estimation methods will be considered. Besides, the closed-loop control strategy based on ammonia coverage ratio and other crucial sates will be designed.S1 File(MAT)Click here for additional data file.


Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area. In addition to radiotherapy and chemotherapy, surgery is one of the major treatment options for brain tumors . HoweverTherefore, despite the improvement in surgery and technology, we are still unable to accurately define brain tumor margins. Label free, non-ionizing imaging modalities that rely on intrinsic properties of tumors or normal brain could be a potential solution to the above problem. Hyperspectral Imaging (HSI) is a form of imaging spectroscopy that captures spectral and spatial data beyond the limited three electromagnetic bands of the human eye . It prodOne of the most active research groups in biomedical applications of HSI is led by Professor Baowei Fei, from the Department of Biomedical Engineer at Emory University. The main characteristics of the HSI research performed by this group can be summarized as follows. Their experiments explore cancer diseases in animal subjects. Until now, they have analyzed prostate cancer  and headIn this pilot study, we investigate whether intra-operative hyperspectral imaging can identify and delineate brain tumors. This work is framed in a European collaborative project funded by the Research Executive Agency (REA) called HELICoiD (HyperEspectraL Imaging Cancer Detection) formed by four universities, two university hospitals and three leading industry partners.® VNIR A-Series model. The VNIR camera covers the spectral range from 400 nm to 1000 nm, with a spectral resolution of 2–3 nm, being able to capture 826 spectral bands and 1004 spatial pixels. This device integrates a Silicon CCD detector array with a minimum frame rate of 90 fps, understanding in this context a frame as a line of 1004 pixels and 826 spectral bands. The lens used in this camera is a Xenoplan 1.4 with 22.5 mm of focal length and a broadband coating for the spectral range of 400 nm to 1000 nm. The camera is attached in a scanning platform composed by a stepper motor and a spindle capable of covering an effective area of 230 mm. This scanning platform is required to acquire the second spatial dimension as the pushbroom camera can only sample a single line. The setup uses an illumination system composed by a Quartz-Tungsten-Halogen (QTH) lamp connected to a cold light emitter via fiber optic that allows achieving cold illumination over the brain surface. This is required in order to avoid high temperatures produced by the QTH lamp over the brain surface. The hyperspectral acquisition system employed in this work is called the HELICoiD demonstrator . The sysA set of five in-vivo brain surface HS images, captured using the previously described acquisition system, has been used for this research. These images belong to adult patients undergoing craniotomy for resection of intra-axial brain tumor. Images have been obtained at the University Hospital Doctor Negrin of Las Palmas de Gran Canaria (Spain) and at the University Hospital of Southampton (United Kingdom) from five different patients with confirmed grade IV glioblastoma tumor on histopathology. The study protocol and consent procedures were approved by the Comité Ético de Investigación Clínica-Comité de Ética en la Investigación (CEIC/CEI) for the University Hospital Doctor Negrin and the National Research Ethics Service (NRES) Committee South Central—Oxford C for the University Hospital of Southampton. Written informed consent was obtained from all subjects. The individual that appears in The procedure to acquire the in-vivo data during neurosurgical operations has been described elsewhere and in summary is as follows. First, after performing the craniotomy and durotomy, the neurosurgeons place some rubber ring markers over the brain surface where they are confident that the tissue inside the markers is tumor or normal based on its macroscopic appearance and taking into account the information provided by the neuronavigator from an MRI scan (Magnetic Resonance Image). In case of patient 1, two markers were placed in the tumor area and one marker was placed over the healthy tissue. In cases of patients 2, 3 and 4, two markers were use, one placed over the tumor area and another one placed over the normal tissue. Finally, in case of patient 5, no markers were used since the tumor was in a deeper layer with respect to the normal tissue and it was clearly identified. After that, the operator of the acquisition system captures a HS image. Depending on the location of the tumor, the images are acquired at various stages of the operation. In cases of patients 1, 2, 3 and 4, one image was obtained immediately after the dura was removed since the tumor was superficially located. In case of patient 5, the image was captured in an advanced stage of the tumor resection since the tumor was deep seated. Once the HS image is taken, the operating surgeon performs a biopsy of the tissue located within the tumor tissue marker/s  or within the clearly identified tumor area (in case of patient 5). The resected tissue is sent to the pathologists in order to confirm the presence or absence of tumor, and obtain the specific histopathological diagnosis (grade and type of tumor). Since this technology cannot penetrate into the tissue (in case of near infrared it can be 1 mm at most), the average size of the resected sample of the tumor for pathological analysis is 0.5x0.5 mm and 0.2 mm depth. Normal tissue markers are only used as a reference for the labelling process performed after the operation has finished. It is not ethic to perform a biopsy of the tissue that is known to belong to normal brain (it can produce damages in the undergoing patient outcomes). Employing the histopathological information (from the tumor tissue samples) and the knowledge of the operating surgeon , the labeling of the HS cubes is performed to generate a gold standard dataset for the supervised classification stage of the brain cancer detection algorithm.® GUIDE application and measures the angle between two high dimensional vectors. This SAM classification is an automated method for comparing the spectra of the pixels of a HS image with a well-known spectrum obtained from a reference pixel. The tool was used by the corresponding operating surgeon after the operation conclusion in order to generate the gold standard map for each image. Four different classes were employed in this study: normal tissue, tumor tissue, blood vessel and background. The procedure to obtain the gold standard map is as follows. First, the user loads a HS cube to be labeled and selects a reference pixel, looking the synthetic RGB representation of the HS cube, at the location where a biopsy is done (where the tumor marker is placed) or at a location far enough from the tumor margins where the surgeon can be quite confident that the tissue is abnormal (in the case of tumor labeling). In case of normal tissue, blood vessel and background classes, the labeling is performed by the operating surgeon by selecting a reference pixel by naked eye based on their knowledge and experience. Then, the most similar pixels to the selected reference pixel are highlighted, based on the SAM measurement, and the user configures the threshold that varies the tolerances on the pixels selected. Once the user considers that only the pixels belonging to one class are highlighted, the selected pixels are assigned to that class. The manual labeling of the HS data consists of visual identification of each sample by a specialist, which is time-consuming task and can introduce errors due to human intervention. For this reason, a methodology for extracting the gold standard information from the HS cubes, based on the Spectral Angle Mapper (SAM) algorithm, has been developed. The tool developed for sample labeling has been designed using MatlabThe classification framework developed in this study aims to exploit both the spatial and spectral features of the HS images. After the acquisition of the in-vivo brain surface HS cube , a pre-pFrom an information-processing point of view, the intrinsic dimensionality of HS images can be significantly reduced before subsequent image characterization steps are applied. Dimensionality reduction maps high-dimensional data into a meaningful representation of reduced dimensional space so that the observed properties of the initial data are still preserved in the low dimensional space. Since the intrinsic dimension, as well as the geometry of the initial data, is unknown, dimensionality reduction, in general, is an ill-posed problem that can only be solved by assuming certain data properties.Thus far, many algorithms for dimensionality reduction have been developed in literature . PrincipFR-t-SNE is an extension of the t-Distributed Stochastic Neighbors Embedding (t-SNE)  that is In the proposed brain cancer detection algorithm, FR-t-SNE is employed to obtain a one-band representation of the pre-processed HS cube with 750 bands (without applying the band averaging step in the pre-processing chain).Support Vector Machines (SVMs) are kernel-based supervised algorithms that have been extensively used for classification tasks. As a relevant example, a variant of the SVM classifier, called Fuzzy SVM classifier, was employed in the development of an emotion recognition system based on facial expression images, obtaining overall accuracy results of 96.77±0.10% . In the SVM algorithm requires a confident labeled dataset in order to train the model that will be used to classify the input data. In this work, the labeled dataset of in-vivo brain samples that is used to train the SVMs has been created by combining the efforts of neurosurgeons and pathologist, as it has been previously described. Before explaining the methodology employed for performing a supervised classification over the available HS data, some considerations have to be taken into account. Due to the impossibility of having a way to extract the labeled information from all the pixels in a HS cube of brain tissue, there are two ways of measuring the performance of the generated supervised models. For the available labeled dataset, it is possible to use standard metrics in order to measure the accuracy provided by the model when classifying unseen data. Nevertheless, for evaluating a supervised model applied to a whole HS cube  only the visual evaluation of an expert is possible. The methodology for evaluating the supervised classifiers in a quantitative way is as follows: first, we use the labeled information corresponding to the dataset, and then we apply a 10-fold cross validation in order to measure the performance of the model. The quantitative evaluation metrics used for this purpose has been sensitivity, specificity and overall accuracy metrics, and will be defined later in this paper.P) and the guidance image (I) . The spatial-spectral feature vector is defined in I is the normalized pixel value (spectrum) at location i and l(i), h(i) are the normalized longitude and latitude of the pixel i. The output of the KNN-filtering is given by Ni refers to the K-nearest neighbors of the pixel i found in the feature space F(i). It can be seen that at λ = 0 there is no spatial information, while when non-zero it captures the spatial information of pixel i given by l(i) and h(i).Once the quantitative metrics have been obtained, the previously trained SVM classifier is used to classify a whole HS cube, and then it is evaluated by neurosurgeons in order to analyze the quality of the algorithm in distinguishing different types of tissues, materials or substances. In order to include the spatial features of the HS images, a spatial homogenization is applied to improve the supervised classification results by incorporating the neighborhood information of each pixel into the classification chain. The algorithm proposed in , which rλ is set to zero, the spatial coordinates are not considered in the KNN filtering process, and when the value of λ increases, the classification results tend to be oversmoothed, decreasing the accuracy of the classification results. The parameter K has a similar influence in the classification results: when the K value is high, the filtering method oversmooths the classification results, worsening the accuracy of the classification results. In this approach, it is not possible to provide a quantitative measure of the influence of K and λ parameters, due to the absence of a complete golden standard map. Nevertheless, the influence of these parameters in the generation of the classification maps has been studied. As mentioned before, large values of K or λ tends to oversmooth the obtained classification maps. Several executions of the KNN filtering were performed employing different values of K  and λ . K and λ. In both cases, small values of K and λ result in a mix of small classes that do not represent the real distribution of the tissues. On the other hand, large values of K and λ tend to oversmooth the classes. After a visual inspection of the results by the specialists (neurosurgeons), the final values of K and λ chosen for this study were K = 40 and λ = 1. These values generate a filtered map where the different classes are homogenized enough without oversmoothing the classification result : Correctly detected conditions. The result of the test is positive and the actual value of the classification is positive.False Positive (FP): Incorrectly detected conditions. The result of the test is negative and the actual value of the classification is positive.True Negative (TN): Correctly rejected conditions. The result of the test is negative and the actual value of the classification is negative.False Negative (FN): Incorrectly rejected conditions. The result of the test is positive and the actual value of the classification is negative.Sensitivity is the proportion of the actual positives that are correctly identified as positives by the classifier , polynomial and Sigmoid kernels have been tested and compared. tumor tissue, normal tissue, blood vessels and background, respectively. The qualitative results generated by the supervised classifier are shown in The supervised classification maps generated in the first step of the cancer detection algorithm have been improved by combining these results with a one-band representation of the HS cube using a KNN filtering method. The one-band representation of the HS image, where the most significant information of the image is revealed, has been generated using the FR-t-SNE, which offers a high contrast value compared to alternative dimensional reduction algorithms. The final stage of the cancer detection algorithm has the goal of combining the segmentation map, obtained by the clustering algorithm, and the spatially homogenized classification maps, generated after the KNN filtering process, to build the final classification map employing the MV algorithm. ® Core™ i7-4770k 3.5GHz) and the time results obtained using the hardware acceleration in the spatial-spectral supervised classification stage. Due to the connection between the computer and the hardware accelerator, a time for the transmission is required in the accelerated version of the algorithm. However, the partition of the algorithm in both platforms allows executing the unsupervised clustering in the CPU and the spatial-spectral supervised classification in the hardware. The total time required for the processing in the accelerated version is computed taking into account the maximum time obtained between the spatial-spectral supervised classification and the unsupervised clustering. Specifically, when the hardware accelerator is not employed, the spatial-spectral supervised classification is the most time consuming stage. In contrast, an average speedup factor of 26.83x is achieved on the spatial-spectral supervised classification stage when the hardware accelerator is used. These results show that the proposed system provides a classification map of the captured scene during the surgery to neurosurgeons in approximately 1 minute, depending on the size of the captured image.In order to assess the application in terms of the processing time required to analyze the HS images during surgical procedures, The task to identify the boundary between the tumor tissue and the normal tissue that surrounds is difficult for neurosurgeons by only using the naked eye, as brain tumors are extremely infiltrative. The current tools employed to this end have many limitations to assist in the delineation of the tumor boundaries. MRI-based neuronavigation accuracy is affected by the brain shift during resection and depends on a questionable correlation between the extent of enhancement on MRI and cellular infiltration. Other techniques, like 5-ALA fluorescence, do not work in low-grade lesions despite being highly invasive and is not recommended for use in children. For these reasons, there is a need to develop new techniques for tumor margin delineation in real-time, maximizing the resection of the tumor and minimizing the resection of the adjacent normal brain. HSI offers a new possibility to address these issues, being a non-contact, non-ionizing and non-invasive technique.In this research work, a methodology to develop a surgical tool for identifying and delineating the boundaries of the tumor tissue using HS images has been described. For processing these data, an active interaction between medical doctors and engineers was required. On the one hand, medical doctors generated the HS image database and the selection of the images where tumor tissue was present. They were also involved in the identification of certain types of tissues, materials and substances that appear in the captured HS cubes. On the other hand, engineers performed the digital processing of the images, developing a brain cancer detection algorithm that exploits the spatial and spectral features of the HS images.The preliminary results obtained in the supervised classification of the tissues that have been previously labeled by the specialists, demonstrate that it is possible to accurately discriminate between normal tissue, tumor tissue, blood vessels and background with an overall accuracy higher than 99%. Using the supervised models generated with the labeled data, the entire HS images were classified and qualitatively evaluated. Five SVM classification maps obtained from five different patients affected by a grade IV glioblastoma tumor were generated. These maps can identify the regions where the tumor is located. Employing a spatial-spectral optimization method based on a KNN filtering and a FR-t-SNE dimensional reduction, the SVM classification maps were spatially homogenized. A clear identification of the tumor regions using this spatial-spectral supervised classification maps is provided. However, these maps do not offer accurate delineation of the boundaries. The unsupervised stage of the algorithm based on a HKM clustering method provides a segmentation map where the boundaries of 24 different regions with similar spectral characteristics are delineated. The fusion of the spatial-spectral supervised classification map and the unsupervised segmentation map through the MV algorithm generates the final classification map, where the boundaries of the different tissues materials or substances presented in the image are identified with a certain class. In summary, the spatial-spectral classification maps allow assigning each cluster in the segmentation map to an identifiable tissue class.Employing the information provided by the MV algorithm, three different ways to represent the final results were analyzed. The MV map assigns the maximum probability of each class to each cluster and represent the cluster with the correspondent color: red for tumor tissue, green for normal tissue, blue for blood vessel/hypervascularized tissue and black for background. On the other hand, the OMD map displays the color of each class degraded according to the value of the first major probability. By using this technique is possible to identify the clusters that conform only slightly to their assigned class. Finally, the TMD map represents each color as a combination between the different classes mixed in a certain cluster. This map is of the most value to the operating neurosurgeon, since it offers the possibility to assess the degree of tumor infiltration into the surrounded normal brain. This assessment is key for judging the desired extent of resection.Finally, this complex algorithm was accelerated in order to obtain the results of the classification in surgical-time during the neurosurgical operation. As a preliminary system, these results are highly promising since this acceleration allows obtaining the classification results in ~1 minute depending on the size of the image, which represents an average speedup factor of 6.43x, with respect to a sequential implementation in a CPU. Compared with the intraoperative pathological analysis or the intra-surgical magnetic resonance, that can take more than 30 minutes, we provide the classification result in ~1 minute, indicating the precise location of the tumor to the operating surgeon in surgical time.The following relevant limitations have been found during this research: a) some false positives have been found on the results; b) there is a need of a clinical validation of the system; c) some misclassifications have been found between different tissues; and d) there is a need of an acceleration of the entire algorithm. Next, these limitations are detailed together with some possible solutions.False positives have been encountered in the obtained results that could be solved with further investigations. For instance, there are some misclassifications between blood or blood vessels and tumor tissue due to the high intra-class variability between the vascularized tissues, although these false positives do not affect the area of identified tumor so that the margins of the tumor remain clearly evident. The use of an increased database to generate the supervised classification model, where the inter-patient variability is taken into account, is expected to produce better classification results. The inclusions of more labeled samples of normal tissue will reduce the occurrence of false positives in the results.Furthermore, an extensive clinical validation is required to validate if the boundaries of the tumor area represented in the TMD map are accurately identified. Several biopsies of the boundaries of tumor area must be obtained and analyzed by the pathologists to certify the brain cancer algorithm results.On the other hand, there are some misclassifications between different tissues with high vascularization. In some cases, extravasated blood and normal tissue affected by edema were classified as blood vessel/hypervascularized tissue (blue color). This misclassification is produced due to the similar spectral characteristics of the blood vessels and the tissue affected by edema. Further investigations where these spectral differences are included in the training of the brain cancer algorithm could alleviate this problem, or perhaps a new class could be created to identify the normal brain with high vascularization.Finally, further investigations in the algorithm acceleration could provide the final TMD map in less than one second by using heterogeneous high performance computing system, thus obtaining real-time results. The future of this intraoperative HSI system is envisioned employing snapshot HS cameras, which can capture about ten images per second, allowing even tracking dynamic changes of the tissues.This study develops a brain cancer detection algorithm to classify HS images of brain tumor in surgical-time during neurosurgical operations. It has been demonstrated that the use of HSI as a new non-invasive surgical-time visualization tool can improve the outcomes of the undergoing patient, assisting neurosurgeons in the resection of the brain tumor. The identification of the tumor boundaries and the tumor infiltration into normal brain is highly relevant in order to avoid excessive resection of normal brain and to avoid unintentionally leaving residual tumor. Currently, further investigations are being carried out by the research team in order to generalize the results obtained, to optimize the algorithms and validate their findings, as well as to increase the image database and optimize the acquisition system. Furthermore, the use of other hardware acceleration platforms (such us GPUs or FPGAs) are currently under consideration to implement the full brain cancer detection algorithm. Such implementation must explore the design space to achieve the best tradeoff between real-time execution, memory usage and power dissipation using heterogeneous platforms. This next generation of medical HSI systems could offer neurosurgeons a real-time visualization tool to assist them during the entire process of the tumor resection providing several TMD maps per second.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.