Resection of the epilepsy foci is the best treatment for more than 15% of epileptic patients or 50% of patients who are refractory to all forms of medical treatment. Accurate mapping of the locations of epileptic neuronal networks can result in the complete resection of epileptic foci. Even though currently electroencephalography is the best technique for mapping the epileptic focus, it cannot define the boundary of epilepsy that accurately. Herein we put forward a new accurate brain mapping technique using superparamagnetic nanoparticles (SPMNs). The main hypothesis in this new approach is the creation of super-paramagnetic aggregates in the epileptic foci due to high electrical and magnetic activities. These aggregates may improve tissue contrast of magnetic resonance imaging (MRI) that results in improving the resection of epileptic foci. In this paper, we present the mathematical models before discussing the simulation results. Furthermore, we mimic the aggregation of SPMNs in a weak magnetic field using a low-cost microfabricated device. Based on these results, the SPMNs may play a crucial role in diagnostic epilepsy and the subsequent treatment of this disease. According to statistics, 30% of epileptic patients are refractory to all forms of medical treatment . In the sing MRI . MRI hassing MRI . Therefosing MRI . In the In this paper, we demonstrate the benefits of the non-functionalized SPMNs as the contrast agent for the complete mapping of epilepsy location in the brain . Indeed,et al. reported [et al. [Despite the significant progress of drug delivery research, the delivery of large drug molecules through the blood-brain barrier (BBB) is still a major challenge. Due to the strong tight junctions, the endothelium patterning around the cerebral microvessels accurately controls the transportation of materials that are necessary for neural signaling. The permeability of BBB can be increased using various chemical techniques ,21. Furtreported  that gol [et al.  have putetc.) about the magnetic field in the brain by offering various theoretical models  and exponopoles ). Based onopoles . Howeveronopoles ,31. In t+), potassium (K+), calcium (Ca2+), or chloride (Cl−). Based on the Hodgkin-Huxley model, and Poisson and Maxwell equations, we can estimate how these electrical activities result in creating a magnetic field  can be derived from a general relationship of magnetic fields generated in a finite length of wires  .(3)Fm→=−4 Ns/m2. It is noteworthy that other forces such as inertia, buoyancy, and gravitational forces as well as the interaction between particles can be neglected for low concentration of SPMNs in a fluid [The fluidic force for a spherical shape particle in a flow is determined by Stokes’s law ,(4)FD→= a fluid . TherefoA and B which can be obtained from the following equations.A and B points with the Cartesian coordinates of  and , respectively. xa and ya are unit vectors in horizontal and vertical directions, respectively. Also, In this sub-section, we put forward a mathematical model to prove that the aggregation of nanoparticles occurs due to the magnetic field generated by neurons. For this purpose, we employ the potential energy function shown in Equation (6) .(6)U=μ0 (9) see .(9)U≃γ2γ, χ are a positive parameter function of current and permeability. As seen in this figure, the minimum value of U occurs when  → .In this section, for simplicity, we present a 2D simulation of SPMNs under the effect of the magnetic field. Based on Equation (5) and energy of SPMNs, the trajectory of each nanoparticle has been derived. In the previous section, we put forward a mathematical proof of aggregation. In this method, based on the polar system, the trajectory of each nanoparticle can be obtained. These simulations have been done in MATLAB. As it shows in effI in Equation (8). As seen in In this section, we discuss the experimental results demonstrating a weak magnetic field generated in the microfabricated coil.The experimental setup includes a micro coil realized on glass using Indium tin oxide (ITO) , a current source (Keithy 2400), and microscope to observe the clusters of SPMNs , 10~15 nm, spherical, 43.8 emu/g saturation magnetization). To pattern ITO, a photolithography technique is used to create a microcoil. As seen in The COMSOL simulation results in By applying a weak current (~1 μA), a weak magnetic field (~2 pT) is generated. Therefore, as expected from Equation (1), the aggregation of SPMNs occurs. Based on the COMSOL simulation results, the magnetic field above the surface of the glass is in the order of (2 pT to 10 nT). The aggregation is achieved by this magnetic field after about less than a minute for all nanoparticles. Based on the experimental results presented in In this section, we briefly discuss the practical considerations associated with the proposed method of epilepsy detection.Aggregation Parameters: The time scale for reorientation and aggregation of nanoparticles depends on various parameters including the strength of magnetic field, the distance of the nanoparticle from the epileptic source and the size of nanoparticles. Based on the simulation results, for an epileptic source with a 2 pT magnetic field, the aggregation of 10~15 nm-sized nanoparticles occurs in a circular area with a diameter equal to 300 micron in 5~10 s. Furthermore, the viscosity of brain tissue in the proximity of the epileptic source is another important factor affecting the time of aggregation and the minimum magnetic field to generate clusters with a specific size.Distribution of Nanoparticles in Brain: The uniform distribution of SPMNs enables the detection of epilepsy foci generated anywhere in the brain. The uniform distribution of nanoparticles into the brain is a key challenge that has been addressed by researchers. Among these researchers, Sonavane et al. reported that the gold nanoparticles with different sizes could be distributed uniformly in the brain [et al. [he brain . Masseri [et al.  have buiMRI-Guided Epilepsy Detection: As already mentioned, MRI is the best method to detect epilepsy in the deep brain using SPMNs. However, an MRI can generate a high strength and high gradient magnetic field that may result in the aggregation of SPMNs. For this reason, in our protocol, the lack of MRI field and gradient effects on clusters suggest that MR images should be acquired postictally . In this case, let us assume the aggregation occurs during the ictal state with high epileptic activity. The clusters with a large number of iron oxide atoms are generated during this phase. Therefore, despite the effect of the MRI on the aggregation of single SPMNs, the magnetic field generated by the MRI is not sufficient to cause movement of the heavy clusters.In theory, the MRI field and gradients have the same ability as any other external magnetic field to cause the aggregation of SPMNs. However, in practice, by optimizing the size and shape of the SPMNs, it is possible to prevent the aggregation of SPMNs exposed to the MRI field and gradient. Additionally, it is assumed that the aggregation and imaging are performed in two separate phases. The first phase includes the aggregation of SPMNs due to high electrical and magnetic activities of epileptic cells. In the second phase, the MRI will be performed to demonstrate the boundary of the epileptic region. Therefore, the MRI field and gradient cannot effectively result in the movement of large SPMN aggregates generated in the first phase.Disaggregation of SPMNs: It is expected that SPMNs disaggregate immediately after an epilepsy attack. However, the hysteresis of iron oxide nanoparticles does not allow the disaggregation to occur rapidly. Similar to aggregation, the disaggregation also depends on several other parameters, specifically, the charges of nanoparticles. Indeed, the disaggregation allows enough time for the MRI to be used for the observation of clusters. Based on the preliminary experiments described in the previous sections, the disaggregation process starts immediately after disconnecting the electrical current from micro coils. In this process the poorly connected nanoparticles are removed from the cluster immediately. However, many nanoparticles remain connected to the cluster, and a sufficient amount of time is required after the current disconnection for the disaggregation of entire SPMNs.The Shape of Nanoparticles: Despite the fact that the gradient of the magnetic field is an important factor in generating force on sphere-shape nanoparticles, the rod-shaped nanoparticles can be used in the very low gradient field ranging from the DC magnetic field. In this study, we have focused on sphere-shaped nanoparticles. Based on our simulation results, the limiting gradient is about 2 pT/um. However, this value of the gradient of the magnetic field depends on the size of nanoparticles and other factors as well.Current and Future Works: As described in the previous sections, this paper outlined the advantage of SPMNs for epilepsy detection. Based on the preliminary experimental results using microfabricated micro coils, the SPMNs can aggregate in a low magnetic field. This paper also suggested that the SPMNs delivered into the brain could be used as an MRI contrast agent for the detection of epilepsy foci. However, several questions remain unanswered and further steps should be taken in this research approach toward a safe and practical clinical protocol. For instance, further simulations and experiments should be performed in order to determine the minimum size of detectable epileptic foci, the minimum required time for the aggregation of SPMNs, the maximum disaggregation time for MRI purposes and other related critical parameters. Although the aforementioned preliminary results in this paper can shed the light on novel technology development for epilepsy foci detection, the optimization of SPMNs as the new MRI contrast agent for epilepsy detection should become the focus of future research. in vitro using a low-cost experimental setup. Based on simulation and experimental results, SPMNs can aggregate in the brain and consequently enhance the related MRI images from the epileptic region. As the continuation of this work, we will develop in vivo experiments using animal models.In this paper, we introduced a novel technique for the detection of epileptic foci. In this new approach, the SPMNs play a significant role as a novel MRI contrast agent for the epilepsy detection. This new technique relies on the aggregation of SPMNs in the brain due to the high magnetic activity of the neural network in the epilepsy zone. Herein we developed mathematical and simulation platforms to prove the concept. We also have demonstrated and discussed experimental results by mimicking the effect of weak epileptic foci


Accurate localization of mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. The proportion factor of distance is also proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. The normalized nearness degrees are considered as the weighted standards to calculate the coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can localize the mobile node in a timely way. The average localization error of PCFL is decreased by about 30.4% compared to the AFLA method. In al (TOA) , angle oal (TOA)  and timeal (TOA) .i.e., projections of the task anchors) as centers, and samples are randomly selected in the overlapping area of the task-rings. Later, the Red Green Blue (RGB) sequences for both the mobile nodes and the samples are computed based on the projection distances. Different from the existing CDL algorithms utilizing the DV-hop measurement [In this article, a projection-color filtering localization algorithm called PCFL and an anchor-color filtering localization algorithm called ACFL are put forward. They both aim at cooperatively accomplishing precise localization for underwater mobile nodes with minimum power wastage. In the first place, the existing network construction is reconstituted as a hierarchical structure and the localization issue is converted into a geometry problem. Secondly, based on the task anchors which can communicate with the mobile node directly, task-rings are obtained considering the task projections  based on a detailed theoretical analysis PCFL and ACFL are proposed for three-dimensional underwater environments; (b) the proposed algorithms adopt the overlapping signal region of task anchors which can reduce uncertainty with fewer anchors than used in other approaches; (c) it is more efficient by using the adapted hierarchical structure and the projection model; (d) as the proportion factor of distance is proposed to weight the RGB values, PCFL and ACFL can provide more accurate RGB values; (e) specifically, the normalized nearness together with the calculation of the coordinates significantly improves the whole computational efficiency.The rest of this paper is arranged as follows: in Some researchers have studied range-free algorithms, which do not need to estimate the distance precisely ,15,16. Iet al. proposed a local sampling and filtering color dynamic localization (LSF-CDL) algorithm [Color-theory based dynamic localization (CDL) , FRORF  and DV-hlgorithm . Using tlgorithm . The locIn the color-theory based localization schemes, a location database is established in the server for building geographic locations as a function of RGB values. The DV-Hop technology is employed in measuring the distances between mobile nodes and anchors. Based on the RGBtoHSV algorithm , the mobCompared to terrestrial nodes which can keep their location unchanged after arrangement, underwater nodes are prone to be influenced by tides, ocean currents and other factors leading to their locations being unfixed . Due to There is a typical UASNs model as shown in With the purpose of developing an accurate and high-performance UABL scheme used in the complex underwater environments, the assumptions are made as below: R and are equipped with arrays of wireless antennas in order to communicate with each other by acoustic signals which are used to transmit the depth information, the anchors’ coordinates and the measured AOA values. Both the anchors and the mobile nodes have the same communication range The mobile node transmits acoustic signals regularly. Once the acoustic signal is received, each anchor replies a message including its own three dimensional coordinates and its depth information.When the information exchange is completed, the mobile node has sufficient information to localize itself. The information refers to locations and the depth information of all task anchors and the AOA measurement from the task anchors estimated by the mobile node. The depth information is measured by the pressure sensors with which all the nodes are equipped.In UASNs, it is difficult to achieve time synchronization precisely as a result of the characteristics of acoustic signal propagation. While by employing the AOA values of the acoustic signals from the anchors, synchronous request between the nodes is not so necessary, so the PCFL and ACFL techniques advanced here have the advantage in synchronization.UN with Let’s consider a 3D UASNs q mobile nodes in We assume that the locations of At time instant Let As all the nodes have the same communication range eference  and respThe task-ring for task projection In this section, we present our color filtering localization algorithms, which we call projection-color filtering localization (PCFL) and anchor-color filtering localization (ACFL). Firstly, we put forward the three dimensional hierarchical structure according to the depth of the anchors and the mobile nodes. Then we introduce the design details of PCFL and ACFL methods. In the end, we show the analysis of feasibility for them. PCFL and ACFL are both based on the color theory. The distances between nodes are calculated by the projection method, while CDL calculates the distance based on DV-hop method. In UASNs, the most important characteristics are node mobility, multipath propagation loss, time uncertainty, and low communication rate. In this paper, our two localization methods are based on hierarchical structure model. Note that the minimum communication angle And at time instant The PCFL algorithm and the ACFL algorithm can be both divided into three steps: (a) determination of the sampling area; (b) RGB values calculation; and (c) filtering and weighted evaluation. (a) In the first step, we use the task-rings sampling method to determine the sampling area. Here every task-ring is expressed as the signal range interface for every corresponding task anchor, as previously defined in Equation (7). Then, the sampling area constraining the location of the mobile node is the intersection region of these task-rings corresponding to each task projection.x, y, z) denotes the coordinates of the sample. Normally, the set of the sampling area For instance, the shadow region in (b) The process of RGB values calculation can be divided into two stages. The first stage is the RGB sequences calculation for the mobile nodes, denoted by ue (HSV)  at time Range is the maximum length of color change, and we assume Range to be the maximum communication distance of the mobile node’s plane. Then Then we can calculate the changed values HSVtoRGB :(15)RijAt time instant At last using the weighted mean of The second stage is the RGB sequences calculation for samples. The RGB sequence for the sample (c) In the process of filtering and weighted evaluation, PCFL and ACFL both filter the samples in the sampling area based on nearness degree. Assume at time instant . The relationship between Then the filtered samples set Assume the coordinate of the filtered sample .The detailed procedure for PCFL and ACFL can be presented by the following Algorithm 1 descriptionAlgorithm 1. The PCFL and ACFL algorithms.if the mobile node then1: 2: ifthen3: 4: 5: 6: 7: end if8: end if9: while10: do11: 12: 13: 14: 15: end while16: ifthen17: 18: end if19: ifksthen20: 21: 22: 23: 24: end if25: From Algorithm 1, it can be seen that PCFL and ACFL are two localization methods for mobile nodes (line 1). In UASNs, nodes communicate with each other using acoustic signals (line 2). If the mobile node receives the feedback signals from the anchors within communication range, then, the anchors are named as the task anchors to localize the mobile nodes . When the mobile node obtains the feedback signals (including the depth information and the locations of the task anchors), the geographic distance The RGB values for task anchors’ projections (PCFL) and task anchors (ACFL) are assigned (line 10). The HSV values for task anchors’ projections (PCFL) or for task anchors (ACFL) are calculated using the traditional convert algorithm RGB to HSV (line 11). Then the HSV values between the mobile node The sampling area can be determined based on the task-rings . Then the RGB values for the samples are assigned (line 20). After that, the nearness degree In this section, we analyze the feasibility of PCFL and ACFL, through the following theorems:Theorem 1. Assume that Proof. As for all Lemma 1. Each RGB sequence is unique. Proof. The lemma can be proved by redectio ad absurbum. Suppose that in PCFL and ACFL there are two different mobile nodes (or samples) with the same RGB sequence at time instant Theorem 2.The smaller the nearness degree Proof. It is clear that redectio ad absurbum. Suppose the sample Then, the theorem can be proved by So:By Equation (13) to Equation (15), we can see:So by Equation (17), we have:Then:However, this is a contradiction to Equation (22). Next we will analysis the time and space consumption in the worst case for PCFL and ACFL.Theorem 3. The new algorithms take O(n) worst case time and O(n) worst case space.Proof. Let’s consider a 3D UASNs UN with p anchors as stated before, in the process (a) determination of the sampling area, it takes O(p) time for the mobile node to obtain the angles w.r.t. each task anchor using the AOA method and it requires O(p) space to store angles. Then it takes O(p) time to compute the distances from the mobile node to every task anchor according to the Equation (9) or to every task projection according to the Equation (10) and it requires O(p) space to store the depth information and O(p) space to store the distance. At last it takes O(p) time to confine the samples and it requires O(p) space to store these samples. p) time for the mobile node to convert the RGB values of the task anchors or of the task projections into HSV according to Equation (13) and it requires O(p) space to store these HSV values. Then it takes O(p) time to calculate the changed HSV values according to Equation (14) and it requires O(p) space to store the changed HSV values. Later it takes O(p) time to work out the changed RGB values according to Equation (15) and it requires O(p) space to store the changed RGB values. Finally it takes O(p) time to work out the proportion factor of distances weights and the RGB values for the mobile node according to Equations (16) and (17) and it requires O(p) space to store the proportion factor of distances weights and the RGB values for the mobile node.In the process (b) RGB values calculation, it first takes O time to work out the nearness degree according to Equation (18) and it requires O(m) space to store the nearness degree. It takes O(m) time to filter out the samples according to the Equation (19) and it requires O(m) space to store the filtered samples. At last it takes O(m) time to normalize weighted the nearness degree and calculate the coordinates of the mobile node according to Equations (20) and (21) and it requires O(m) space to store the normalized weighted nearness degree and the coordinates of the mobile node.In the process (c) filtering and weighted evaluation, assuming sampling n) time and O(n) space in the worst case to estimate the coordinates of the mobile node. Therefore, in total, both PCFL and ACFL can be considered as taking OEitSimulation parameters in this paper are summarized in We compare localization performance of PCFL and ACFL through three different variables which are the threshold, the density of anchors and the number of samples.Filtering out more samples for ACFL (PCFL) in the process of simulation experiments can improve the localization error, but consumes more energy. Considering this situation, the number of samples which are deployed to localize mobile nodes should be reasonable to reach a compromise between energy savings and localization accuracy. In When the number of the deployed samples which are filtered out to localize mobile nodes is much enough, samples are concentrated in the vicinity of the mobile node and the localization error reduces obviously. We can find that the localization error for both PCFL and ACFL is 0.91 m and 4.44 m when the number of the deployed samples is 500, respectively. R and is assumed to change from 5 to 100. We assume that In anchor/V , so we cIn the simulation space, 45 mobile nodes are randomly distributed to localize themselves, the distances between each pair of them are 120 m and 160 m. As we can see in We can find that localization error of PCFL (or ACFL) reduces slightly when the number of samples is more than 400. When In It is obvious that total energy consumption for either PCFL or ACFL is increasing with the density of anchors. Energy consumption for ACFL is more than that for PCFL. This also verifies the high energy efficiency of PCFL.In Here, we run the simulation 50 times for each localization error in Equation (29), then the average localization error can be obtained. We compare the five algorithms as shown in The localization error of the Anchor-based algorithm is susceptible to the error of the estimated distances in relation to the coordinates of the fixed anchors, so the maximum localization error, the minimum localization error and the standard deviation of the Anchor-based algorithm are larger than the other four algorithms. Utilizing geometrical relationships, the AUV-aid method performs localization coarsely, so its average localization error is the biggest. We can see that the average localization error of the ACFL is 4.56, which is larger than that of the AFLA and the PCFL according to the simulation results. The speed of mobile nodes is an important factor affecting the localization error. Here the speed of mobile nodes is set to vary from 2 m/s to 20 m/s. The results of the simulation experiments as shown in One hundred mobile nodes whose locations are unknown are randomly deployed in the three dimensional localization region. As we can see, in the process of simulation experiments, the five algorithms could localize all the deployed mobile nodes. Because the angles between task anchors and mobile nodes are calculated using the AOA method, the estimated distances between them have low accuracy and the localization error rises obviously with the number of the deployed mobile nodes increasing. The localization error of PCFL is smaller than that of the other four methods in The average localization error of AUV-aid is bigger than the other four methods. The error variation of the Anchor-based method fluctuates clearly and reaches the maximum value. Compared with ACFL, PCFL is an effective self-localization algorithm, its localization error changes stable and keeps the smallest in the localization process.In this paper, two algorithms called PCFL and ACFL using the color filtering method have been proposed for mobile node self-localization in UASNs. The PCFL method is based on the RGB values of task anchors’ projections, while ACFL is based on the RGB values of task anchors. The two methods can improve the coarse localization by combining the CDL method with the AOA measurements. Also the proportion factor of distance can optimize the CDL method and the nearness degrees can help filtering samples more precisely. The proposed methods present better accuracy and robustness than the Anchor-aid, AUV-aid and AFLA methods, especially the PCFL method, when the speed of the mobile nodes is lower. We plan to expand this work with actual underwater experiments and reduce the localization computational complexity in the future.


The FAMULATUR PLUS is an innovative approach to teaching physical examination skills. The concept is aimed at medical students during the clinical part of their studies and includes a clinical traineeship (English for “Famulatur”) extended to include various courses (“PLUS”). The courses are divided into clinical examination courses and problembased-learning (PBL) seminars. The concept’s special feature is the full integration of these courses into a 30-day hospital traineeship. The aim is to facilitate the transfer of knowledge from the courses into daily practice. Each week of the FAMULATUR PLUS is structured in line with the courses and focuses on a particular part of the body . A physical examination course under the supervision of a physician is offered at the beginning of the week. Here, medical students learn the relevant examination techniques by practicing on each other (partner exercises). Subsequently, the techniques taught are applied independently during everyday work on the ward, corrected by the supervisor, if necessary, and thereby reinforced. The final POL seminar takes place towards the end of the week. Possible differential diagnoses are developed based on a clinical case study. The goal is to check these by taking a fictitious medical history and performing a physical examination, as well as to make a preliminary diagnosis. Finally, during the PBL seminar, medical students will be shown how physical examination techniques can be efficiently applied in the diagnosis of common cardinal symptoms . The initial implementation of the FAMULATUR PLUS proved the practical feasibility of the concept. In addition, the accompanying evaluation showed that the participants of the pilot project improved with regard to their practical physical examination skills. The physical examination of patients – alongside the assessment of their medical history – is an essential medical skill. Approximately three quarters of all diagnoses can be made based solely on the medical history and physical examination , 2]. De. De2]. DThe objective of the project presented here is the intensive transfer of physical examination skills to medical students. This should be done as part of a clinical traineeship, which is complemented by an integrated teaching program (FAMULATUR PLUS). From the perspective of a medical student, a clinical traineeship is the practical addition to his/her theoretical studies. The FAMULATUR PLUS was developed based on this conception and is composed of three didactic elements – the physical examination course, the clinical traineeship, and the PBL seminar – described in more detail below. The temporal sequence of the didactic elements follows a standardized structure (see 3.4). The FAMULATUR PLUS includes a total of five physical examination courses. These courses are intended to take approximately 120 minutes each and are taught by physicians. The teaching and learning objectives of all physical examination courses are guided by the current literature , 8], , , 11], , . The firAlthough the emphases of the physical examination courses vary depending on the instructor and the topic in question, this does not apply to the curricular structure. The sequence of the physical examination courses is thus always the same and can be divided into four steps – demonstration, deconstruction, comprehension, and implementation – following a modified Peyton’s technique , 15]. B. B15]. BThe clinical traineeship is a key component of the proposed concept, seeing as it allows the transfer of knowledge from the physical examination courses to the daily work on the ward. Every week, practical exercises are assigned to encourage the medical students to apply the learned examination techniques. These exercises can be completed flexibly by the medical students. Every week, at least three general internist and three organ-specific examinations, which correspond to the respective physical examination course, are to be completed. If the examination course “Head and Neck, Thorax, Spine and the Lungs” took place in the first week, for example, the medical students are required to complete three general internal examinations and three examinations of head, neck, thorax, spine, and lungs in the corresponding week. This procedure also applies to all other weeks of the clinical traineeship. The medical students receive medical history and examination forms on which they can briefly document their examination findings. The completed medical history and examination forms are submitted to the ward physician for review and must be countersigned by him/her. The review of the medical history and examination forms ensures thatthe medical students are “forced” to physically examine the patients anddifficulties in the practical implementation of the examination techniques are revealed and can serve as the basis for the optional feedback session with the ward physician (see 3.4).In addition, the ward physician is called upon to provide the medical students with constructive feedback regarding their examination skills as well as to identify opportunities for improvement on a weekly basis. Furthermore, the medical students are free to check their own performance by comparing their examination findings with the medical findings documented in the patient’s chart. Problembased-learning (PBL) constitutes the third didactic element alongside the physical examination courses and clinical traineeship. Five PBL seminars take place during the FAMULATUR PLUS. Numerous differential diagnoses are initially postulated based on a clinical case study. The number of differential diagnoses is to be gradually reduced down to a concrete diagnosis on the basis of a fictitious anamnesis and physical examination. After completion of the PBL, the medical students are responsible for independently performing follow-up course work. Such follow-up work may, for example, consist of developing a diagnostic algorithm for a particular cardinal symptom . On the one hand, the PBL and follow-up serve to show the importance of the physical examination for the diagnostic process. On the other hand, the PBL teaching method is well suited to improve the clinical thinking of the medical students by practicing problem solving processes , 16]. A. A16]. Ahttp://fakultaet.medizin.uni-ulm.de/fileadmin/Studiengaenge/Humanmedizin/Informationen_Lehren-Lernen-Track_SS15.pdf; accessed on 2 December 2014].Student tutors are responsible for teaching and moderating the PBL seminar. Starting from the summer semester of 2015, didactically trained tutors (“peer teachers”) from the “Train the Tutor” training program of the Faculty of Medicine at the University of Ulm will be used in the FAMULATUR PLUS , as, as5], aThe evaluation of the pilot sample, comprising five medical students, showed some initial trends: for example, the medical students’ average assessment of their practical physical examination skills in the advance questionnaire corresponded to a grade of 4 (with 1 being the best and 6 being the worst), while their average assessment after completion of the FAMULATUR PLUS corresponded to a grade of 2. A similar picture emerged for the auscultation and physical examination tests. Across tests, approximately 75 percent of the requirements were met by the medical students after completion of the FAMULATUR PLUS; prior to completion, only approx. 26 percent were met.In an initial pilot project, the FAMULATUR PLUS proved to be a feasible teaching method for imparting physical examination skills. Transfer of the concept to other hospitals is planned. Furthermore, potential benefits of the FAMULATUR PLUS for the practical training of medical students are being discussed. Evaluation of previous data showed an improvement in the practical physical examination skills of participating medical students. However, the significance of these results is limited given the small sample size and lack of a control group. As a result of this, modification of the study design as a randomized and controlled study appears sensible . MoreoveThe authors declare that they have no competing interests.


Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications. The Global Navigation Satellite System (GNSS), which is a crucial means for navigation and positioning for both civil and military applications, is playing an increasingly significant role in modern society. However, the GNSS signals are extremely fragile and vulnerable, and can easily be influenced by interferences . Therefoetc. [The hypothesis testing method establishes hypothesis testing statistics with monitoring indicators, and obtains the threshold of the monitoring indicators with the given significance level. In , Balaei etc. ,3. The hetc. This method requires a monitoring threshold, which is usually derived from the corresponding characteristic of the GNSS signals without interference. However, the GNSS signal environment is always complex, and especially during transfer, the GNSS signal inevitably suffers from intentional or unintentional interferences. Therefore, utilizing the characteristic without interference as the threshold will increase the false alarm rate and missing report rate.The method based on characteristic parameter threshold compares the actually measured indicators or monitoring quantities with a preset threshold in order to determine whether there are interferences. The monitoring indicators include the quantization weight gain of the A/D converter , power sIn 2011 Faurie and Giremus proposed an interference monitoring method based on GNSS and Inertial Navigation System (INS) , where GWilson and Ganguly proposed in 2006 a GPS interference monitoring method based on 3D Controlled Reception Pattern Antennas (CRPA) , where aAmong all these methods, the hypothesis testing method and the method based on characteristic parameter thresholds require theoretical values or models without interferences. However, due to the complicated environment, the time-varying and uncertain GNSS signal can make it difficult to precisely establish models and determine the theoretical values. As a result, we cannot just rely on the traditional methods to establish the necessary precise models to monitor the interferences. In addition, the integrated navigation method and smart antenna technology require superior stability of systems or superior parameter deployment.On the other hand, applying the GNSS signals will accumulate huge amounts of data, which contain the influences of interferences on the signal quality and receiver performance. We can monitor the interferences without the precise physical models if the information and knowledge can be mined from these data. This method based on GNSS signals, seeks out regularities from these data, and then utilizes these regularities to monitor the unknown or unobservable data, without any need to know the precise analytic models of the systems in advance, thus making this method a more intelligent and flexible one. At present, the data-driven method has achieved successful applications in fields such as fault diagnosis and fault prediction. In this paper, the data-driven method is introduced into the field of interference monitoring, transforming interference monitoring problems into artificial intelligence classification problems, then used to monitor the interferences.etc., are extracted from the GNSS signals, and then are imported into an artificial intelligence classifier to determine whether there are interferences, and to acquire the information such as the type and strength of the interferences.This paper presents an interference monitoring method, where the parameters manifesting the characteristics of interferences, such as the power spectrum, carrier to noise ratio, the correlator output, The selection of the classifier has a crucial impact on the accuracy and efficiency of monitoring. Support Vector Machine (SVM) is chosen as the classifier in this paper. SVM is one of the state-of-the-art classification techniques. Benchmarking studies reveal that the SVM performs the best among current classification techniques .Solving of SVM involves quadratic programming, and the computational complexity rapidly increases as the training samples increase in number. In this paper, the Twin Support Vector Machine (TWSVM) technique is utilized to ensure the interference monitoring is real-time. TWSVM has outstanding classification precision, faster than SVM, and it satisfies the accuracy requirement of the GNSS interference monitoring. Therefore, this paper will focus on the GNSS interference monitoring with the TWSVM technique.The rest of this paper is organized as follows: in the following section an introduction to SVM is presented. The model of TWSVM and the LSTWSVM algorithm are presented in We assume that the training sample set of the interference monitoring is The SVM aims to build a classification hyperplane m by SVM  as folloLet As is shown, the classifier decided by Equation (4) only relies on the samples The common method to solve Equation (2) is the Sequential Minimal Optimization (SMO) algorithm . HoweverUnlike the standard SVM, TWSVM constructs two hyperplanes Supposing that a n-dimension sample set posed in , and e1,The computational complexity of the standard SVM is Currently, there are three methods to solve the TWSVM: the Sequential Minimal Optimization (SMO) algorithm , the SucBased on the fact that the Support Vectors are almost distributed around the classification hyperplanes, LSTWSVM narrows the constraint of the optimization problem near to the classification hyperplane, and modifies the original optimization models Equations (6) and (7) to Equations (8) and (9), respectively. Because of the relaxation factor Substituting the constraint into the objective function, Equation (8) becomes: Differentiating Equation (10) for Combining Equation (11) with Equation (12), and adapting it into matrix form, then we have: Defining Similarly: The process of the interference monitoring based on TWSVM is shown in etc. The post-correlation interference monitoring is implemented by observing the carrier to noise ratio, correlator output, etc.From Frequency characteristic is the most direct reflection of signal characteristics. Through observing the frequency characteristic of GNSS signals, a variety of information can be obtained, including signal bandwidth, power spectral density, in-band stray, and interference performance. Therefore, the analysis of frequency characteristic is firstly carried out in interference monitoring. Power Spectral Density (PSD) reflects the signal power distribution within the signal bandwidth. With the measurement of the PSD, whether the power of every frequency is well-distributed or there is abnormal power can be seen. PSD is also an effective means to monitor the GNSS signal interference.Carrier to noise ratio  level and the classification speed of TWSVM is on microsecond (μs) level, which can be used in practice.


Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances. Defects may occur in the stress concentration areas of in-service components or systems. Generated defects gradually grow due to stress and pose a severe threat to the structural integrity of in-service parts or systems ,2,3. TheCharacterization of a defect is to find a solution to an inverse problem, and has to be completed before evaluation and decision-making concerning maintenance and replacement . In manyet al. [et al. [et al. [et al. [There are many factors that affect the performance of model-free based characterization of defects. Extensive investigations have been conducted to improve defect characterization with regard to reduction of liftoff effect, new design and optimization of probes, and feature extraction ,15. The et al.  normaliz [et al.  theoreti [et al. ,20,21. J [et al.  fabricat [et al.  evaluate [et al. ,25. Prin [et al. . To over [et al. . Kernel  [et al. .In addition to the aforementioned factors, excitation strategy plays an important role in defect characterization as well. At present, single frequency, multiple frequency and pulsed excitation are presented to acquire more information on defects ,28,29. HThe purpose of this paper is to enhance surface defect classification by seeking the optimal excitation frequency. In order to determine the optimal frequency, the influences of excitation frequency on characterization performances for a set of defects were disclosed and interpreted in terms of detection sensitivity, contrast between extracted features and accuracy of classification. The main contribution of this work lies in the experiment-based procedures for determining the optimal frequency applied to a group of defects in terms of characterization performances. The remainder of this work is arranged as follows: KPCA for feature extraction and support vector machine (SVM) for classification of defects are briefly introduced in PCA is a statistical multivariate analysis technique. It transforms original observed data into a new set of uncorrelated variables. Each derived variable termed as a principal component is a linear transformation of original observed data. In theory, KPCA is a nonlinear extension of standard PCA using kernel methods . As a rexi into a feature space F using a nonlinear transformation Assuming there is a set of data sampled from probe signals corresponding to a given scanning route F are centered, i.e., C in the space F is calculated byGiven that the data k and vk stand for non-zero eigenvalue and eigenvector of the covariance matrix C, respectively.We need to find Eigenvalues and Eigenvectors satisfyingSubstituting Equation (3) into Equation (2), we haveFrom Equations (4) and (5), we haveK, respectively.Defining the kernel function Finally, the resulting kernel principal components can be calculated usingK is that we can cope with The elegance of using the kernel matrix plicitly .The classification problem can be restricted to consideration of the two-class problem without loss of generality. In this application, the goal is to produce a classifier that will generalize well in unknown cases. The support vector machine (SVM), founded by V. Vapniks, is a pattern recognition technique based on structural risk instead of empirical risk minimization. Therefore, an SVM has better generalization capabilities, and suitable for small sample size.S correctly. However, only the optimal hyperplane in canonical form can maximize the margin between the two classes, which is formulated asGiven a dataset The margin could be derived byHence, the optimal hyperplane is determined by minimizing the following function with the constraints of Equation (9)To solve Equation (11), we define the Lagrange function belowC are introduced. A kernel function is adopted to map observed data into a high-dimensional feature space in order to transform a linearly non-separable case to a linearly separable case. As a result, Equations (13) and (14) becomeC is a penalty factor, and The aforementioned formulation of the optimal hyperplane is restricted to linearly separable cases. However, this will not be the case in general. To deal with linearly non-separable cases, a kernel function and penalty factor i is obtained, the decision function used to classify sampled dataset from sensors isAfter the variable αThe automated experimental setup developed for eddy current characterization of defects is composed of an impedance analyzer, a probe, a 3D scanner and a PC, as shown in Two Al alloy specimens were prepared, and ten artificial slots were manufactured by electrical discharge machining (EDM) for training and test datasets, as illustrated in In this section, we will experimentally assess the influences of excitation frequency on the classification of surface defects in light of detection sensitivity, dissimilarity of defect features and classification accuracy. As the probe is fixed on the 3D scanner, the liftoff distance is kept constant during inspection. The probe moves in a direction perpendicular to the defects. After multiple trials, scanning distance is set to 10 mm with a step of 0.1 mm, and scanning speed is 0.1 mm/s. Therefore, we can obtain 100 sampled data points from a single scan. Each data point stands for coil impedance consisting of resistance and reactance. The excitation frequency, which is much greater than the frequency corresponding to the standard penetration depth, should be chosen to increase detection sensitivity, as we simply focus on surface defects in this work. The excitation frequency ranges from 50 kHz to 850 kHz in step of 100 kHz.In order to analyze the effect of excitation frequency on defect sensitivity, experiments were carried out to acquire probe signals due to the given sets of the defects varying in depth and length, as shown in Further analysis shows that the excitation frequency, at which maximum probe signals are collected, should vary with the size of defects. For example, the probe signals due to the defect D1 increase with the increase of the excitation frequency from 50 kHz to 850 kHz. This indicates that maximum probe signals for the defect D1 shall be observed when the excitation frequency is higher than 850 kHz. In contrast, the probe signal due to the defect D5 reach maximum at the frequency of 550 kHz. This finding implies that we cannot simultaneously acquire all the maximum probe signals for different size defects at a single frequency. We also note that probe signals change significantly before reach of the peak, and decrease slightly afterwards.In this subsection, we aim at disclosing the influences of excitation frequency on the defect features. With the KPCA presented in Here, all the manufactured defects are divided into two groups. Group 1 consists of the defects with different depths, while group 2 is composed of the defects with different lengths. All the features were reprocessed using the KPCA method. The features for group 1 and group 2 were shown in In this subsection, we focus on the classification accuracy at different frequencies. At first, a SVM classifier was used to evaluate the accuracy when excitation frequency ranges from 50 kHz to 850 kHz. Then, the findings from the evaluation were validated by an artificial neural network (ANN) classifier.C and select an appropriate kernel function, which has great influence on the performance of an SVM classifier. The penalty factor C represents the penalty for misclassification. It controls the trade-off between the achievements of training and testing errors, which can generalize the classifier of unseen data. The larger the penalty factor C, the greater the penalty for training errors. In other words, we risk losing the generalization properties of an SVM classifier if we increase the penalty factor C too much, because the SVM classifier will try to fit as much as possible for all the training points. Currently, there are linear, polynomial and radial basis function (RBF) kernels for selection [C and parameters of RBF kernel function. In this work, the penalty factor C was set 0.00097.The key to the use of an SVM classifier is to determine the penalty factor election . Many reelection . In ordeUsing the extracted features with KPCA, the SVM classifier was trained on the training dataset, and subsequently evaluated on the testing dataset. The classification results are listed in To confirm the influences on classification accuracy, ANN and SVM classifiers combined with PCA and KPCA methods were adopted at all frequencies, as shown in (1)Determine the range of the defects to be identified before inspections;(2)Manufacture a sample defect with a maximum depth and length;(3)Observe probe signals when excitation frequency is adjusted continuously until maximum signals are retrieved for the fabricated sample defect;(4)The optimal frequency should be equal to the frequency corresponding to the observed maximum probe signals.As described in the previous section, the performance of surface defect classification would be the optimal if we employ the optimum frequency at which the probe signals reach the maximum for the largest defect. For a single defect, the theoretical evidence of the existence of peak frequency corresponding to maximum probe signals could be derived from the references ,34. The The optimal frequency should be the frequency or near the frequency at which the maximum probe signal is retrieved for the largest defect. It is noted that the findings in this work only apply to characterization of surface defects.Part of the research activity has been concentrated on the development of novel excitations, such as multiple sinusoidal signals, pulse and chirp signals ,42. CompScanning angles are supposed to change probe signals significantly, thus leading to degradation of defect characterization . To our In this paper, experiment-based procedures have been proposed to determine an optimal frequency over a group of defects with conventional eddy currents. This work is characterized by frequency optimization with respect to characterization performances rather than only detection sensitivity. In addition, this work features frequency optimization over a set of defects and not for a single defect.For enhancement of surface defect characterization, the influences of excitation frequency have been identified in terms of detection sensitivity, contrasts between defect features and classification accuracy. When excitation frequency was equal to or near the frequency at which maximum probe signals were obtained for the largest defect to be inspected, probe signals from a set of defects were most sensitive on the whole. Defect features were extracted from probe signals using the KPCA algorithm. It was found that the retrieved defect features were evenly spaced, and the margins between them were the largest. According to the structure risk minimization (SRM), which is a statistical learning theory concept, the largest margins imply the lowest misclassification probabilities. Consequently, the SVM classifier, which operates on SRM, trained by the extracted features shall possess greater generalization capabilities. The experiments confirmed the best characterization performances at the selected optimal frequency, which strengthens the motivations behind the proposed techniques.It should be noted that this paper is confined to surface defects. Future research will concentrate on determination of optimal excitation frequency for subsurface defects and the eddy current imaging technique to eliminate signal changes resulting from variant scanning angles.


Scientists often use a paired comparison of the areas under the receiver operating characteristic curves to decide which continuous cancer screening test has the best diagnostic accuracy. In the paired design, all participants are screened with both tests. Participants with suspicious results or signs and symptoms of disease receive the reference standard test. The remaining participants are classified as non-cases, even though some may have occult disease. The standard analysis includes all study participants, which can create bias in the estimates of diagnostic accuracy since not all participants receive disease status verification. We propose a weighted maximum likelihood bias correction method to reduce decision errors.Using Monte Carlo simulations, we assessed the method’s ability to reduce decision errors across a range of disease prevalences, correlations between screening test scores, rates of interval cases and proportions of participants who received the reference standard test.The performance of the method depends on characteristics of the screening tests and the disease and on the percentage of participants who receive the reference standard test. In studies with a large amount of bias in the difference in the full areas under the curves, the bias correction method reduces the Type I error rate and improves power for the correct decision. We demonstrate the method with an application to a hypothetical oral cancer screening study.The bias correction method reduces decision errors for some paired screening trials. In order to determine if bias correction is needed for a specific screening trial, we recommend the investigator conduct a simulation study using our software. Paired screening trials are common in cancer screening. For instance, one of the designs considered for a planned oral cancer screening study was a paired comparison of the visual and tactile oral exam with the VELscope imaging device . Two recIn paired cancer screening trials, investigators screen all participants with both screening tests. The screening tests may measure a participant’s disease status with error. To ascertain participants’ disease states more definitively, the study investigator tests each participant with a second, more accurate procedure. We refer to the definitive procedure as a reference standard test. In cancer screening, the most accurate reference standard test is biopsy followed by pathological confirmation of disease. Biopsy is painful and invasive and can only be performed on individuals with a visible lesion. Thus, the study investigator determines participants’ disease states as follows. Participants with unremarkable screening test scores on both screening tests enter a follow-up period. Participants with suspicious screening test scores or who show signs and symptoms of disease during follow-up undergo further workup leading to a reference standard test. Participants who do not show signs and symptoms of disease during follow-up are assumed to be disease-free. Follow-up can be thought of as an imperfect reference standard. The reference standard is imperfect because, in truth, some participants may have occult disease.et al.[In the trial by Lewin et al., the invet al.. If the Screening trials are subject to different biases depending on the choice of reference standard and analysis plan -8. PaireWe propose a bias-correction method to reduce decision errors in paired cancer screening trials. Under the assumption that the screening test scores follow a bivariate Gaussian distribution, conditional on disease status, we use an iterative, maximum likelihood approach to reduce the bias in the estimates of the mean, variance and correlation. The resulting estimates are then used to reduce bias in the estimates of the diagnostic accuracy of the screening tests.In the following sections, we describe the bias correction method and evaluate its performance by simulation. In the Methods section, we explain the study design of interest, outline the assumptions and notation, delineate the bias correction algorithm and describe the design of the simulation studies. In the Results section, we report the results of the simulation studies and demonstrate the utility of the method using a hypothetical oral cancer screening study. Finally, in the Discussion section, we discuss the implications of the results and provide recommendations.The study design of interest is a paired study of two continuous cancer screening tests. A flowchart of the study design is shown in Figure true disease status of each participant. The second viewpoint is that of the study investigator, who can only know the disease status observed in the study.We consider the screening study from two points of view . The firobserved disease status as follows. Any score that exceeds the threshold of suspicion defined for each screening test triggers the use of a reference standard test. Cases identified due to remarkable screening test scores are referred to as screen-detected cases. Participants with unremarkable screening test scores on both screening tests enter a follow-up period. Some participants may show signs and symptoms of disease during the follow-up period, leading to a reference standard test and pathological confirmation of disease. These participants are referred to as interval cases. We refer to the collection of screen-detected cases and interval cases as the observed cases. Participants with unremarkable screening test scores who do not show signs and symptoms of disease during the follow-up period are assumed to be disease-free, or observed non-cases.The study investigator determines a participant’s Misclassified cases occur when study participants who actually have disease receive unremarkable screening test scores and show no signs or symptoms of disease.We present a graph of a hypothetical dataset of screening test scores  or absence (k=0) of disease. The pair of screening test scores, Xi1k and Xi2k, are independently and identically distributed bivariate Gaussian random variables with means μjk, variances ρk.Let aj be the threshold of suspicion for screening test j. All scores above the threshold will trigger the use of a reference standard test. For screening test j, the percent ascertainment is 100 times the number of participants with disease who score above the threshold on screening test j, divided by the total number of participants observed to have disease.Let I be the event that a participant shows signs and symptoms of disease and P(I|k=1)=ψ. Because participants without the target disease are unlikely to show signs and symptoms of that disease, we assume that P(I|k=0)=0. In practice, however, the clinician must respond to any signs and symptoms with further testing, even if those signs and symptoms may not, in fact, be caused by the target disease. Thus, participants who show signs and symptoms of disease during the follow-up period will still receive the reference standard test and subsequent pathological confirmation.Let We describe an algorithm to reduce bias in estimates of diagnostic accuracy. The algorithm corrects the maximum likelihood estimates of the parameters of the distribution of case screening test scores. The algorithm then uses a weighting scheme to reduce the variance of the estimates. The corrected maximum likelihood estimates are used to calculate corrected estimates of the diagnostic accuracy of the screening tests.The algorithm requires four steps.A (data in the gray area) be the set of true cases with at least one screening test score above its respective threshold. Let B (data in the white area) be the set of true cases where the scores on both screening tests fall below their respective thresholds. The percentages of participants observed to have the disease in sets A and B differ: all cases in set A are observed, but only a fraction of cases are observed in set B. The estimation for each set is handled separately in Step 2. Then, in Step 3, the estimates are combined using weighting proportional to the sampling fraction  and the formula . The Type I error rate of the corrected analysis was closer to nominal than that of the observed analysis, but was also too high (0.12 to 0.95). For pairings with no paired screening trial bias, the observed analysis had lower than nominal Type I error rates while the corrected analysis had Type I error rates up to 0.26. When both screening tests had high percent ascertainment (80/80), the Type I error rate of the corrected analysis was below nominal.In general, when the study had some amount of paired screening trial bias (as indicated by a difference in the percent ascertainment), the Type I error rate of the The results of the non-Gaussian simulation studies are summarized below. A table of the main results is presented in Additional file corrected analysis had a lower Type I error rate than the observed analysis for multinomial bin sizes 1/4 the standard deviation or less. At low disease prevalence, the Type I error rate for the corrected analysis was lower than the observed analysis for multinomial bin sizes 1/10 the standard deviation or less.At medium and high disease prevalence, the true analysis remained below nominal. The Type I error rate of the corrected analysis, however, was above nominal for all disease prevalences and bin sizes greater than 1/10 the standard deviation. The observed analysis had an inflated Type I error rate at medium and high disease prevalence. At low disease prevalence, the Type I error rate of the observed analysis was below nominal except at a multinomial bin size of 2 times the standard deviation.For the range of multinomial bin sizes considered in the study, the Type I error rate of the true and corrected analyses were closer to nominal than that of the observed analysis.For zero-weighted data, the success rate of the Nath algorithm decreased as the percentage of zero scores for the cases increased. At low disease prevalence, when 1% of the cases had zero scores, the Nath algorithm converged for only 33% of the simulated trials. For zero-weights less than 1%, the Type I error rate for all three analyses was above nominal at medium and high disease prevalence. However, the Type I error rate of both the Figure true areas under the curves for Test 1 and Test 2 were fixed at 0.77 and 0.71, respectively.We could find no published oral cancer screening trials of paired continuous tests. Instead, we chose parameter values from a breast cancer screening study  and an oIn the hypothetical oral cancer screening trial, we posit that there would be a large difference in the percent ascertainments for each screening modality. In the first arm, the dentist only recommends biopsy for participants with highly suspicious lesions. Thus, we fixed the percent ascertainment to be very low, only 0.01% of the cases. The oral pathologist recommends biopsy for almost any lesion so we set the percent ascertainment at 97% of the cases. The large difference in percent ascertainment created extreme paired screening trial bias, causing the receiver operating characteristic curves to switch orientation relative to the truth.true difference in the areas under the curves was 0.06 (p = 0.001) and the observed difference was - 0.06 (p = 0.005). The corrected analysis realigned the curves with the true state of nature, adjusting the difference back to 0.06 (p = 0.001).When there is an extreme amount of differential bias, the method performs well Figure . The trucorrected analysis was below nominal at 0.03, while the Type I error rate of the observed analysis was above nominal at 0.06. The correct rejection fraction of the corrected analysis was 0.58, while that of the observed analysis was zero. In fact, using the observed analysis, the study investigator would wrongly conclude that Test 2 was superior to Test 1 86% of the time. Based on this simulation, we would recommend the study investigator use the results of the corrected analysis.In reality, the study investigator would not know which analysis had results closest to the truth. To validate our choice of analysis, we simulated the hypothetical study using the parameter values specified above. The simulated Type I error rate of the e.g., [We could find no other methods that attempted to ameliorate paired screening trial bias. Re-weighting, generalized estimating equations, imputation and Bayesian approaches have been proposed to reduce the effect of partial verification bias e.g., -27). Max. Maxe.g.e.g., ,29 and le.g.,  have beeThe bias correction algorithm is a maximum likelihood method. Thus, the accuracy of the estimation depends on the number of cases. We do not recommend using the method for studies with a very small number of cases (<500) and interval cases (<5). The performance of the method improves as the disease prevalence and rate of signs and symptoms increase because both factors increase the amount of information (number of cases) used to form the corrected estimates. As the disease prevalence becomes very large, however, the benefits of the increased amount of case information is constrained by the increasing number of non-cases. The non-case parameter estimates are not corrected and add bias to the estimates of diagnostic accuracy.observed difference in the areas under the curves. Under the conditions of the study, the average difference in the observed areas under the curves was zero at an approximate correlation of 0.7. At higher correlations, the average observed difference was underestimated but agreed with the true state of nature. At lower correlations the bias was more severe: the average observed difference was overestimated and opposite the true state of nature. The bias correction method performed best under conditions with a large amount of differential bias. Thus, at low correlations the corrected analysis had lower Type I error rates and higher power for the correct decision relative to the observed analysis.For the correlation simulation study, the performance of the method depends upon the observed study results be used if: 1) all participants receive a reference standard test, or 2) the two screening tests under consideration ascertain approximately the same percentage of cases. Both situations are plausible in cancer screening. In a proposed oral cancer screening trial [The simulation studies demonstrated that the performance of the bias correction method depends on the amount of differential bias in the study. The amount of bias, in turn, depends on fourteen factors: the means, variances and correlations of the test scores, the disease prevalence, the rate of signs and symptoms and the percent ascertainment for each screening test. After analysis of over 170,000 combinations of the parameter values, we were unable to determine a definitive pattern upon which to base recommendations for using a standard versus a bias-corrected approach. We can, however, provide recommendations for two special cases. We suggest that ng trial .In order to determine if bias correction is indicated for a screening trial that is not a special case, we recommend the investigator conduct a simulation study similar to those described in the manuscript. The simulation software, instruction manual and example code are available at . The sofUnder most circumstances, the study investigator should choose the analysis that has the Type I error rate closest to, but not greater than the nominal level, highest correct rejection fraction and lowest wrong rejection fraction. In some contexts, one type of error may be more important than the other. Controlling the Type I error rate is a priority if there is only one study that is going to be performed and patients could be put at harm if the wrong screening test is selected. A small inflation of the Type I error rate might be less important if there is prior knowledge that the null is not true. For example, say a researcher is designing the last study in a series of studies examining complimentary hypotheses. If all previous studies rejected the null hypothesis, then the researcher has prior knowledge that the phenomenon may show an effect. In this situation, the researcher might prioritize the analysis with a slightly higher than nominal Type I error rate in favor of greater discriminatory power under the alternative hypothesis.Another limitation of the method is the assumption that screening test scores are distributed bivariate Gaussian conditional on disease status. The bivariate Gaussian distribution is the underlying assumption for the binormal receiver operating characteristic curve, a popular form of receiver operating characteristic analysis . We evale.g., [This paper provides two contributions to the literature. First, we describe a method to correct for paired screening trial bias, a bias for which there is no other correction technique. Due to the increasing use of continuous biomarkers for cancer detection , a growThe proposed bias correction method reduces decision errors in the paired comparison of the full areas under the curves of screening tests with Gaussian outcomes. Because the performance of the bias correction method is affected by characteristics of the screening tests and the disease being examined, we recommend conducting a simulation study using our free software before choosing a bias-corrected or standard analysis.The authors declare that they have no competing interests.BMR conducted the literature review, derived the mathematical results, designed and programmed the simulation studies, interpreted the results and prepared the manuscript. TAA assisted with the literature review and provided expertise on the context of the topic in relation to other work in the field. JTB assisted with the mathematical derivations. SMK provided guidance for the design and programming of the simulation studies. AM improved the software and packaged it for public release. KEM reviewed the intellectual content of the work and gave important editorial suggestions. DHG conceived of the topic and guided the development of the work. All authors read and approved the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/14/37/prepubEffect of the rate of signs and symptoms. The file contains results for the simulation study examining the effect of varying the rate of signs and symptoms on the Type I error rate and power of the true, observed and corrected analyses.Click here for fileNon-Gaussian simulation study. The file contains the main results for the simulation study examining the robustness of the bias correction method to deviations from the Gaussian assumption.Click here for file


Collectively, viruses have the greatest genetic diversity on Earth, occupy extremely varied niches and are likely able to infect all living organisms. Viral infections are an important issue for human health and cause considerable economic losses when agriculturally important crops or husbandry animals are infected. The advent of metagenomics has provided a precious tool to study viruses by sampling them in natural environments and identifying the genomic composition of a sample. However, reaching a clear recognition and taxonomic assignment of the identified viruses has been hampered by the computational difficulty of these problems. In this perspective paper we examine the trends in current research for the identification of viral sequences in a metagenomic sample, pinpoint the intrinsic computational difficulties for the identification of novel viral sequences within metagenomic samples, and suggest possible avenues to overcome them. While genomics is the research field relative to the study of the genome of any organism, metagenomics is the term coined for the research that focuses on many genomes at the same time, as typical in some sections of environmental studies. The analysis of microbial communities has been until recently a complicated if not untractable task due to their high diversity and to the fact that many of these organisms cannot be cultured. Harnessing the major advances achieved in sequencing technologies, metagenomics has emerged as the only currently available approach to extensively characterize these largely unculturable communities. Besides vastly enriching our knowledge of microbial diversity in a varied range of environments, and providing information on the dynamics and on the overall functioning of microbial communities, metagenomics is also shedding light on many important biological processes and, in particular, on the role of the microbiome in biological functions essential for the development of higher order organisms harboring it  in a given environment. When it comes to species identification, the task is called taxonomic assignment. Current NGS technologies have provided an opportunity for doing this analysis routinely  a given sequence belongs—that we call first-level assignment—from a more fine-grained taxonomic assignment at, e.g., family, genus or species level. In virome studies the latter task is greatly facilitated when targeted sequencing of purified viral particles is performed  is of clear interest as evidenced, for example, by the discovery of the Mimiviruses with genomes exceeding in size those of many bacterial genomes  sequence similarity methods and (2) sequence composition methods.k-mers (words of size k) and conceptually rely on the fact that when k is sufficiently large, k-mers become very specific. Consequently, the idea is to index the databases by long k-mers. This is indeed the foundation of MegaBlast , but also of a number of methods specific for taxonomic assignment such as LMAT  and index-based. Alignment-based methods suffer from two limitations: speed and lack of sensitivity  to train a classifier on a reference database. The taxonomic assignment of novel sequences is then predicted by applying the pre-trained model. These methods theoretically are better suited to the task of novel species classification as short k-mers distributions are less prone to over-fitting. However, even these techniques fail to classify about 50% of species absent from the training set  sequences, none of the existing methods produces a taxonomic distribution that is even remotely close to the expected one . This has been an effective way to circumvent the difficulty of the first-level assignment, albeit without solving it.For bacterial communities the most efficient solution is to perform a tag survey, where only partial genomic information is used and the sequencing is performed for marker genes, such as 16S rRNA for prokaryotes and 18S rRNA for eukaryotes (fungi). This simplifies the analysis for two reasons. First, the amount of data remains reasonable  and second, known marker genes' taxonomic classification is available through reference taxonomies such as RDP . Composition-based techniques seem to provide satisfactory results for fine-grained taxonomic classification of filtered viral samples  or to a class ;Given a sample of bacterial sequences, to assign each of them to a phylum ; andGiven a sample of viral sequences, to assign each of them to a group  or to a family .k-mer frequency vectors.The classification tasks (1) and (2) are fine-grained assignment problems and mimic characterization of targeted metagenomic studies; while task (3) represents a first-level assignment and mimics the analysis of complex, untargeted environmental samples. Since we are interested in the identification of novel species in large metagenomic samples, we adopted the representation of sequences as k-Disagreeing Neighbors (kDN), which measures for a given instance the number of k nearest neighbors that do not share its class label. Smith demonstrated that the kDN measure is strongly positively correlated with the misclassification of an instance over a wide range of learning algorithms and of training data resampling.We analyzed these three classification tasks using an instance-level analysis of data complexity. In supervised machine learning, the performance of a classifier is dependent both on the learning algorithm  and on the training data. While global metrics recapitulate overall performances of a classifier, they fail to indicate whether moderate performances are a consequence of wrong parameter adjustments, biased resampling for training data or of the intrinsic difficulty of the classification task. However, recent literature on instance misclassifications demonstrates that for a given classification task, some instances are intrinsically hard to classify and that their presence is indicative of the global difficulty  dimensional vectors. For each contig, its kDN value is the number of other contigs that do not share its class label among its closest 73 neighbors. The corresponding class hardness is then measured as the median kDN of all the contigs in a given class. We also determined whether an observed median kDN is significantly extreme , by estimating the distribution of the median kDN under the null hypothesis of no relation between class labels by random permutations.To compare the classification hardness of the three tasks, we generated from a representative subset of sequenced organisms from Genbank  100 sets of 10,000 randomly chosen contiguous genomic fragments of 500 nt average length (corresponding to the average size of metagenomic contigs to simulate an assembly step). For task (1), only bacterial genomes were considered, for task (2) only viral genomes were considered, while for task (3) a balanced composition of viruses, archaea, bacteria and eukaryotes were considered. Each sequence was represented as a 3-mer frequency vector  and we defined the distance between two contig as the Euclidean distance between their respective 64 ; and of viruses known to infect them. Using principal component analysis (PCA), we projected the 3-mers frequencies vectors of these contigs on 2 dimensions. Figure It has been previously observed that viral 3-mer signatures are close to that of their hosts (Pride et al., k-mer distributions overlap with cellular one's almost indiscriminately. This is to be contrasted with the relative ease of the corresponding classification task for archaea and bacteria that certainly underlies the success of bacterial taxonomic assignment studies. The difficulty for viral sequence classification will be alleviated as the public sequence databases become further populated with acquired viral data but this will not provide a sufficient solution to the problem of novel species discovery.Distinguishing viral and cellular sequences in non-targeted environmental studies is a yet unresolved classification problem, especially for unknown viral species. We have shown that the reason why this problem has been eluding a satisfactory solution lies in its intrinsic computational difficulty. The reason for this difficulty lies in the fact that viral sequences k-mer indexing.We strongly believe that appropriate choice of computational methodology and further research efforts in this direction are key for the advancement of this field. In the current state of knowledge, we recommend adopting the strategy of contig-level assembly of reads combined with k-mer frequency-based analysis for the identification of viral sequences in metagenomic samples. As for the development of new methods, the promising avenue for the discovery of novel viral sequences seems to be the relaxation of the stringency of long The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.