This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to  Jamming threats are increasing in the Global Navigation Satellite System (GNSS) bands. According to recent Eurocontrol Voluntary ATMincident reports , the numGiven the increased prevalence of interferences in GNSS bands, the focus in the GNSS literature in recent years has shifted towards integrity methods and procedures to deal with jamming and other interference signals, such as meaconing and spoofing. There are several jamming management solutions, such as detection, mitigation, localization, or classification. The focus in the literature so far has encompassed all-but-jamming classification solutions. For example, jamming detection solutions were reported in ,3,4; jamThe paper’s contributions are as follows: (i) proposing to treat the jammer classification as a black-and-white image classification problem, based on the spectrograms computed at the GNSS receiver from the Intermediate Frequency (IF) samples; (ii) proposing efficient Support Vector Machines (SVM) and Convolutional Neural Network (CNN) based classifiers with classification accuracy of 90% or more; (iii) offering to the research community an open-access comprehensive database of clean and jammed GNSS signal spectrograms for a variety of Carrier-to-Noise Ratio  jammers. We would like to emphasize that the AM noise jammers are different from the AM/CW simple jammers described here; our AM jammers refer to single- or multi-tone sinusoidal signals, weighted with various amplitudes, as shown below in Equation . AM jammThis category contains jammer signals whose frequency is modulated linearly over time. They are generated by sweeping linearly their frequency over a certain frequency range and a certain time period, after which the process is started again at the initial frequency. These saw-tooth chirp jammers can be modeled by Equation . The parFM jammers can also be single-tone or multi-tone. An FM jammer can be modeled using Equation , for whiThese are jamming signals that are active only during repetitive periods of time with an active period of a pulse called the “duty cycle”. Pulse jammers can be modeled using Equation , where pThe narrowband noise jammers are jammers that span a narrow band of the signal spectrum, e.g., by sending a higher power signal in that spectral region. Such jammers can be modeled using Equation , where βThe different jammer signals described in To obtain the plots in In this article, we propose a methodology in order to identify and classify the different jammer types based on the analysis of such a spectrogram image. The methodology is depicted in the block diagram of The choice of the machine learning algorithms for classification was also based on literature studies. We selected SVM and CNN, as described in In this section, the implemented machine learning methods to perform the classification are briefly described. For the SVM method, proposed for example in Vapnik ,15, we bVocabulary building: First of all, the features from all the training images were extracted. These features were stored in a “visual vocabulary” dictionary, where each feature represented a “visual word” or “term”.Term assignment: After extracting the features, they were gathered into clusters. The clusters collected the closest terms in the vocabulary dictionary in order to reduce the complexity.Term-vector generation: The term vector was generated by recording the counts of each term that appeared in the image to create a normalized histogram counting the times it was repeated in the cluster. This term vector was the bag of features representation of the image.A bag of features method consists of representing images based on extracting local features. The name comes from the Bag of Words (BoW) representation used in textual information retrieval . AnalogoThe set of features contained in the term vector were used by SVM to classify the data into the different classes.SVM consists of a set of supervised learning methods that can be used for classification, regression, and/or ranking. Supervised learning means that the user must take part during the training process of SVM. The user must label the training data previous step of training SVM. SVM uses machine learning theory as a principle of operation to learn and to perform classification. SVM performs classification by transforming (if needed) the original training data into multidimensional space and constructing a hyper-plane with it. The established hyper-planes will split the different classes to classify. The hyper-plane with the maximum distance between support vectors, which are the closest data points to the hyper-plane, is the hyper-plane used to classify, and it is called the optimal hyper-plane. Therefore, our goal was to maximize the margin between the edge data points and the hyper-plane in order to be able to classify with the minimum miss-classification error. In order find the optimal hyper-plane, we could start defining a hyper-plane as:Among all the hyper-planes separating the data Equation  as:(8)maThe weight vector To find the optimal hyper-plane, different methods can be used. Some of the most popular are the Iterative Single Data Algorithm (ISDA) , L1 softTypically, most classification tasks cannot be separated as easily as in There are multiple kernel types that we could use to transform the data. Some of the most popular ones are the linear kernel, the polynomial kernel, and the Radial Basis Function (RBF) kernel.The linear kernel is defined as:The polynomial kernel could be expressed as:Finally, the RBF kernel is defined as:The kernel choice in our studies is discussed in Input layer: converts the input image into Convolutional layer: computes the output number of neurons that are connected to local regions of the input image by computing a dot product between their weights and a small region they are connected to in the input. The convolutional layer will have ReLU layer: also known as the Rectified Linear Unit layer (ReLU), applies an element-wise activation function, such as Pool layer: This layer will perform a downsampling operation along the spatial dimensions (width and height). For example, in our architecture, the pool size was , which means that if the layer returns the values Fully connected layer: The objective of a fully connected layer is to take the results of the convolution/pooling process and use them to classify the image. The fully connected layer goes through its own back propagation process to determine the most accurate weights. Each neuron receives weights that prioritize the most appropriate label.Softmax layer: This layer limits the output of the previous step to classification into the range zero to one. This allows the output to be interpreted directly as a probability.Classification layer: computes the cross-entropy loss for multi-class classification problems with mutually exclusive classes. In other words, it performs the classification based on the output of the previous layer.A CNN or Convolutional Neural Network (ConvNet) consists of a sequence of layers, where every layer transforms the data into a simplified version through a differentiable function. The three main types of layers to build ConvNet’s architectures are the convolutional layer, pooling layer, and fully connected Layer. Our layer architecture, as is shown in Stochastic Gradient Descent with Momentum (SGDM) optimizer .Root Mean Squared Propagation (RMSProp) optimizer .Adaptive moment estimation (Adam) optimizer .To perform the training, we need a solver. Three of the most common are:Both Machine Learning (ML) algorithms described in In order to create the databases, a large set of independent simulations was carried out, as described in The whole database was then split in three parts: 6000 images for training purposes , 1800 images for validation purposes , and 54,000 images for testing . Thus, the dataset was split as Generating the GNSS signal plus one of the jammer types at a time, using random signal parameters following a certain uniform distribution, as is summarized in Sending the GNSS signal (with or without jammer) over a wireless channel with Additive White Gaussian Noise (AWGN). The Computing the spectrogram of the received signal and saving it as a black-and-white image in the training, validation, or testing datasetApplying the SVM and CNN algorithms described above to classify the test images, based on the information contained in the training and validation image databases.Computing the confusion matrix, counted as the percentages of correctly classifying each jammer type  and the percentages of misclassifying a jammer type The simulations steps were the following:The training process with SVM was built on a In terms of kernel choice, we used the RBF kernel since it works well in practice and it is relatively easy to tune. In order to find the optimal hyper-plane, we used the Sequential Minimal Optimization (SMO) method , which iThe CNN was trained during 25 epochs with the Adam algorithm  due to iThe meaning of the different colors of each label box is summarized in the right side color bar. The confusion matrix shows how accurate a classifier is, in terms of how well it classifies and misclassifies the test dataset in the different classes it has.Similarly, We also noticed that, by using CNN, the accuracy determining the presence of a jammer (versus no jammer case) was slightly higher than for SVM, although only by The pulse jammers’ classification accuracy was also similar to that of SVM, namely close to 100%. The chirp jammers’ accuracy was more than 91%, but also about 5% lower compared with SVM. The classification of AM and FM jammers showed an accuracy of almost 91% and 89%, respectively . Finally, the poorest classification accuracy was obtained for the NB jammers, where we obtained an accuracy of only 78% .The dataset of images used to achieve these results, as well as the MATLAB codes to run the simulations using both ML algorithms are available in open access at Zenodo .This paper presented a methodology to classify jammer types, treating this as an image classification problem. To do so, a dataset composed of 61,800 different images was generated. The dataset contained different jammer types (including the no-interference scenario), as well as randomly generated parameters for these interference types. The parameters were modeled following uniform distributions in order to contemplate a wide amount of different scenarios. The set of generated images was used to train two different ML algorithms: an SVM and a CNN.The results showed that with a small library of images and not excessively complex parameters/network layer architectures, we could achieve a pretty high mean classification accuracy of more than 90%; specifically, To summarize, we can say that given the low amount of images used for training the algorithms and given the low complexity used during training, surprisingly good results could be obtained. Further improvements can be focused on increasing the complexity of the training layers so the classification can be more accurate; especially to distinguish AM and FM interferences much better, in addition to including and trying to differentiate between single and multi-tone AM and FM jammers.


In the last decade, fingerprinting localization using wireless local area network (WLAN) has been paid lots of attention. However, this method needs to establish a database called radio map in the off-line stage, which is a labor-intensive and time-consuming process. To save the radio map establishment cost and improve localization performance, in this paper, we first propose a Voronoi diagram and crowdsourcing-based radio map interpolation method. The interpolation method optimizes propagation model parameters for each Voronoi cell using the received signal strength (RSS) and location coordinates of crowdsourcing points and estimates the RSS samples of interpolation points with the optimized propagation model parameters to establish a new radio map. Then a general regression neural network (GRNN) is employed to fuse the new and original radio maps established through interpolation and manual operation, respectively, and also used as a fingerprinting localization algorithm to compute localization coordinates. The experimental results demonstrate that our proposed GRNN fingerprinting localization system with the fused radio map is able to considerably improve the localization performance. With the rapid development of information technology, especially the emergence of electronic maps, location-based service (LBS) has been one of current research hot-spots and has been applied to various aspects of people’s daily life, bringing great convenience to people . As a coThe basic WLAN fingerprinting localization method consists of two stages: the off-line and on-line stages . In the However, one disadvantage of the fingerprinting localization method is that the process of establishing the radio map is labor-intensive and time-consuming . FurtherIn this paper, we also apply crowdsourcing to radio map establishment. In the target region, we select a few locations as crowdsourcing points (CPs) and attach two-dimensional (2-D) code stickers at these CPs on the ground. These 2-D code stickers are near the toilet, elevator, and exits, where it is convenient for the crowdsourcing participants to obtain the location coordinates of CPs through scanning these 2-D code stickers. Thus, the crowdsourcing participants are able to collect the RSS data and location coordinates of these CPs and then upload these location-labeled RSS data to a localization server for radio map establishment through interpolation. Using a Voronoi diagram, the target region can be partitioned into Voronoi cells according to the locations of CPs and the propagation model parameters are optimized with crowdsourcing data in each cell so that more accurate RSS data can be estimated. In this way, the proposed Voronoi diagram and crowdsourcing-based radio map interpolation method reduces the labor and time cost for radio map establishment greatly compared with the traditional radio map establishment method using manual operation. To the best of our knowledge, little literature has combined Voronoi diagram and crowdsourcing for radio map establishment.We propose an interpolation method for radio map establishment based on a Voronoi diagram and crowdsourcing. The method first partitions the target region into Voronoi cells according to the locations of CPs using a Voronoi diagram. The propagation model parameters in each Voronoi cell are optimized with the RSS data and location coordinates of CPs. Then the RSS data of selected interpolation points (IPs) in each Voronoi cell are estimated with the optimized propagation model parameters and are calibrated according to the RSS data of CPs. So a new radio map can be established through the proposed interpolation method.We propose a GRNN-based fingerprinting localization algorithm, which fuses the two radio maps, consisting of the RSS data and location coordinates of the RPs and IPs, respectively. Then a nonlinear function between the RSS data and location coordinates is approximated by the GRNN using the fused radio map. In the on-line stage, the nonlinear function is used to compute the localization coordinates.We verify the proposed localization system with the RSS data and location coordinates collected from a real indoor environment. The experimental results show that our proposed Voronoi diagram and crowdsourcing-based radio map interpolation method for GRNN fingerprinting localization system is effective in saving radio map establishment cost and improving localization performance.In this paper, we propose a Voronoi diagram and crowdsourcing-based radio map interpolation method for GRNN fingerprinting localization using WLAN to offer accurate localization results for users in indoor environments. Although the disadvantage of this work is that it is based on a basic fingerprinting localization system with an original radio map, it not only needs no extensive time and labor cost to establish a new radio map, but also achieves a comparable localization performance. The main contributions of this paper can be summarized as follows:The remainder of this paper is organized as follows: One of the key components of WLAN fingerprinting localization is to establish the radio map, so people have paid great attention on it. Currently, most of the research on radio map establishment is based on crowdsourcing. P. Bolliger  first prIn addition to the above-mentioned crowdsourcing method for radio map establishment, other radio map establishment methods have also been proposed. J. Yin et al.  reconstrFingerprinting localization algorithm is essential to WLAN fingerprinting localization. Besides the fingerprinting algorithms referred to above, like the similarity measurement-based algorithms KNN and WKNN, fingerprinting algorithms based on cosine similarity  and PearIn recent years, many advanced fingerprinting localization algorithms have been proposed. J. Talvitie et al.  proposedIn short, fingerprinting localization can be formulated as a regression problem that is to predict location coordinates with RSS data using an approximated nonlinear function. Because GRNN has a superior regression performance and appropriate noise attenuation performance, which is usually used for function approximation , we applAs the basic fingerprinting localization system, our proposed localization system can also be generally divided into two stages: the off-line stage and on-line stage, which is shown in In the off-line stage, we also establish a radio map with the RSS data and location coordinates of RPs in the indoor environment, which we consider the original radio map. We select a few extra locations as the CPs and attach 2-D code stickers with location information printed on them, so that the crowdsourcing participants can scan and obtain the location coordinates to label the RSS data measured at these CPs. We exploit the Voronoi diagram to partition the indoor environment into small Voronoi cells. In each Voronoi cell, there is only one CP and the RSS data and location coordinates of the CP are used for optimizing propagation model parameters. We compute the mean value of each propagation model parameter for interpolation. After the locations of IPs are determined around the CP and RPs in the Voronoi cell, the RSS values of different APs are estimated for the IPs with the optimized propagation model parameters and then calibrated with the RSS data of the CP. When the computation and calibration of RSS data at all the IPs are finished, a new radio map is established through interpolation. Then, we utilize a GRNN to fuse the two radio maps and approximate to a nonlinear function In the on-line stage, when the user takes a mobile device and measures an on-line RSS sample A Voronoi diagram is also known as a Thiessen polygon or Dirichlet tessellation, which offers an effective solution for partitioning a plane into a few regions . It has We take the CPs as the sites and let As we know, because wireless signal attenuates with the increasing propagation distance, RSS data measured in indoor environments are spatially related. When we partition the indoor experimental environment into several Voronoi cells according to the locations of CPs, the RSS data collected in the same Voronoi cell usually have the similar RSS characteristics. This is because the wireless links between the APs and locations in the same Voronoi cell could be influenced by the same factors, such as the building structure, furniture, and presence of humans.Thus, we optimize propagation model parameters with the known location-labeled RSS data of the CP and calculate the RSS data with the optimized propagation model parameters for the IPs in the same Voronoi cell. In this paper, we adopt a common propagation model  to estimWe let The distance Because the location coordinates of the APs and CPs are known, the real distance between each AP and CP can be computed. We let The propagation model parameters In this paper, the initial values of the propagation model parameters lgorithm  for the Due to the blockage of walls, doors, furniture and so on, the wireless signals from some APs at some locations may be so weak that they cannot be detected by mobile devices, which causes the corresponding RSS values are unavailable and equal to 0 in the mobile devices. When we estimated RSS values with the optimized propagation model parameters, few estimated RSS values at the IPs are 0. Thus, we need to calibrate the estimated RSS data according to the RSS data of CPs measured in the real environment. For example, if all the RSS values from After the RSS calibration, the estimated RSS data, along with the location coordinates of all the IPs, can be compiled into a new radio map. Because GRNN does not need iterative training like the MLP trained by back propagation (BP) algorithm and it has a superior regression performance to MLP or RBF network , we use As shown in In the summation layer, there are two kinds of neurons. One kind of neuron calculates the weighted sum In this paper, we collected all the experimental data from a real office environment. The experimental environment was a rectangular region with dimensions of 51.6 m × 20.4 m, as shown in As mentioned above, we selected 10 CPs in the experimental environment, so the experimental environment is partitioned into 10 regions with a Voronoi diagram according to the locations of the CPs, with each region being a Voronoi cell. The partitioning result is shown in Then we select 128 specific locations in the experimental environment as IPs and estimate RSS data for these IPs. These IPs are around the RPs and CPs, and the locations of the CPs, RPs, and IP are shown in We first take one trajectory as an example, the localization results with the fused radio map computed by the traditional KNN and proposed GRNN algorithms are shown in We also compute the localization results using all the RSS data at TPs and the fused radio map, and then compare the localization performance of the fingerprinting algorithms KNN, WKNN, MLP, and our proposed GRNN. We set the same parameters for the KNN, WKNN, and GRNN as in the simple trajectory experiment. Regarding the MLP we utilize, it is trained by the BP algorithm in the off-line stage. The numbers of neurons in the input layer, hidden layer, and output layer of the MLP are 7, 40, and 2, respectively. The number of iterative epochs is set to be equal to 10,000. Meanwhile, Sigmoid function and linear function are selected as the activation functions for the hidden layer and output layer of the MLP, respectively.The localization results are listed in In this paper, we propose a WLAN fingerprinting localization system, which is not only able to save labor and time cost for radio map establishment, but also achieves a comparable localization performance. In the off-line stage, we exploit the Voronoi diagram and crowdsourcing to establish a new radio map through interpolation. We first partition the experimental environment into Voronoi cells using the Voronoi diagram according to the locations of CPs, and then optimize the propagation model parameters with the CP data, so that the estimated RSS data using optimized propagation model parameters after RSS calibration will be more similar to the RSS data measured in the real environment. Because the radio map is established based on crowdsourcing data collected by crowdsourcing participants, it can be easily updated according to the RSS variations in the indoor environment. Owing to the superior performance of GRNN for function approximation, we use a GRNN as a fingerprinting algorithm to fuse the new radio map established through interpolation and the original radio map and to approximate to a nonlinear function between RSS data and location coordinates. Then localization coordinates can be computed using the nonlinear function in the on-line stage. The experimental results show that our proposed fingerprinting localization system can reduce the mean error of localization results to 2.78 m with the crowdsourcing data collected at only a few CPs, which proves the effectiveness of the proposed system. We expect that the proposed system could offer a valuable reference for WLAN fingerprinting localization and promote the theoretical research and practical application of WLAN fingerprinting localization to some degree.In the future, we intend to continue the work on radio map interpolation to reduce labor and time cost for radio map establishment. We might also focus on the crowdsourcing-based localization performance improvement against device diversity, multi-signal fingerprinting localization, and high-accuracy fingerprinting localization based on deep learning.


Extracting fundamental information from data, thus underlining hidden structures or removing noisy information, is one of the most important aims in different scientific fields especially in biological and medical sciences. In this article, we propose an innovative complex network application able to identify salient links for detecting the effect of Alzheimer’s disease on brain connectivity. We first build a network model of brain connectivity from structural Magnetic Resonance Imaging (MRI) data, then we study salient networks retrieved from the original ones.Investigating informative power of the salient skeleton features in combination with those of the original networks we obtain an accuracy of The experimental results demonstrate how salient networks can highlight important brain network characteristics and structural pathological changes, while reducing considerably data complexity and computational requirements. Alzheimer’s disease (AD) is a progressive neurodegenerative disease accounting for most cases of dementia after the age of 65. AD onset is related to cognitive impairment and loss of memory and it is estimated that over 115 million people will develop AD by 2050 [AD related brain changes can be detected in vivo with magnetic resonance imaging (MRI) that has been playing an increasingly important role for the early diagnosis of neurodegenerative disorders. Progression of AD can begin even decades before the clinical and radiological manifestations of the illness. Indeed, it is known that dementia is preceded by a prodromal phase of mild cognitive impairment , and thiIn fact, standard imaging techniques with structural MRI have allowed the observation of gray matter reductions especially in the hippocampus, the enthorinal cortex and the para-hippocampal gyrus in both temporal lobes –7. As a Machine learning techniques remain unsurpassed in terms of accuracy and yet these strategies seem the best chance to get an early diagnosis for AD. However, complex networks, can be a convenient and innovative instrument to describe the connectivity of both structural and functional brain networks and detect anomalies yielded by disease , 15 lessNumerous studies, across a different range of anatomical parts of the brain, scales and modalities have found that networks may show a behavior outcome of a combination of both regularity and randomness . In factCurrently, many studies emphasized as scale-free and small-world topologies might be used to model human brain networks and as a consequence describe how brain connectivity is affected by AD. In fact, it was shown that several diseases, such as Alzheimer’s disease, disrupt small-world organization and make human brains more adherent to random or regular networks. However, nothing definitive has been established, yet. Besides, another interesting aspect reported in literature is the vulnerability of network hubs to targeted attacks, these latters often are related to the progression of the disease in the network .In this study, for each MRI T1 brain scans, we modeled brain connectivity dividing automatically brain into a fixed number of boxes, called patches or supervoxels, to determine the nodes of the network and measuring pairwise Pearson’s correlation for all supervoxels to define the network links. Thus, a weighted and undirected dense network was built for each subject scans. One of the major aims of this work is the identification of hubs or highways, which could be modified by the disease. Therefore, it was carried out a study to define a proper threshold to bring out the two main topologies of the brain networks of which peculiar elements are really the most connected nodes and the most important links. , 20. At The paper includes: a section of "Image processing to achieve an intensity and spatial normalization among subjects;Network construction for each subject having supervoxels as nodes and their Pearson’s correlations as links;Detection of the salience links to retrieve the salient skeleton from the original network;Construction of the multiplex network of the salient skeletons to extract some features in order to assess informative power of the skeletons using supervised learning techniques.The proposed approach includes four main steps shown in Fig. In this work we used a training set of 67 T1 MRI scans, composed of 29 normal controls (NC) and 38 AD subjects, from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). These subjects belonged to a larger benchmark dataset  selected3, without a preliminary segmentation. As shown in previous works [Firstly, MRI scans were intensity normalized to reduce inter-subject differences. Then, FSL-BET and FSL-FLIRT, from FMRIB library , were apus works –26, the Background should be small-world and scale-free especially for biological networks [m to examine, and the reference random graph with the same node number N and the same link probability p given by the ratio between mean degree N. Once found the appropriate threshold able to bring out the two main topologies, the high salience skeleton was extracted for each network in order to investigate which connections between are more relevant. Fig. The image processing and network construction were performed for each subject, thus a set of densely connected weighted networks was obtained. To reduce complex networks to their main components is an usual practice to examine a wide range of threshold values and then, according to specific experiment goals, choose the criterion for finding the most suitable one. A possible strategy to use for this investigation concerns the network topology which as disclosed in the networks . Therefoentclass1pt{minimaN nodes remaining after the extraction of the scale-free and small-word topologies from the network, the pairwise distance matrix D is defined. i and j are connected through a path p consisting of k steps, being them the terminal nodes of p, the length of p is simply the weight sum of the edges belonging to it. Accordingly, it is possible to define several paths connecting i and j, we considered the so-called shortest paths that are the paths of which weight sum is maximum. It has to be noted that according to this definition, the shortest path uniqueness is not assured. Recording all links belonging at least one time to the shortest paths connecting a generic reference node n to all the remaining nodes of the network, we can define for each reference node what is generally called the shortest path tree T(n) a matrix which represents, in fact, the most effective routes linking the node n to the network. Its elements i, j) connecting the nodes i and j, salience i and j. Salient matrix values have a characteristic bimodal distribution with peaks on 0 and 1 values, as shown in Fig. i, j) is essential for all reference nodes, if i, j) is not a fundamental link for the network. This makes natural finding what is called the salience skeleton, i. e. the backbone structure including all the most efficient links of the networks.The salience indicator was born to furnish an overall network description from a node-specific perspective and define a consensus among nodes on the importance of each edge within the network. Given the set of weights Figure Each salient network was employed for building a skeleton multiplex network (SMN). For each layer, nodes are represented by the supervoxels that have a salient link at least in a subject and links, that change for each layer, are the absolute value of Pearson’s correlation between nodes pairs within the single layers.O whose elements From the SMN were extracted some centrality measures: the strength and the inverse partecipation ratio, first considering the single layer links, and then the multiplex network links. Multiplex network strength and inverse partecipation ratio were defined starting from an overall matrix In addition, for this 4 quantities we computed the corresponding conditional means, thus strength and inverse participation averaged over the nodes with the same degree.Overall we retrieved a matrix of 8N features × M samples, where N = 549 is the number of nodes of the SMN and M = 67 is the number of layers of the SMN.To avoid over-training issues yielded by the excess of features compared to observations, a feature selection was performed with a wrapper-based method. The most important features were collected for each fivefold Random Forest cross-validation and then, those having a significant probability of occurrence within 1000 rounds, were selected. Random Forest was grown with 500 trees and the important features were chosen in order to overcome the third quartile of the importance distribution computed in terms of mean accuracy decrease.IQR are respectively the third quartile and the interquartile range of the betweenness distribution values of a generic network. On the base of this definition, hubs of the salient network and the original one were collected in order to see how many hubs are preserved moving from the original network to the salient one. In addition, for both the original network and the salient one we computed two metrics: strength and betweenness associated to the hubs of each subject to examine if these metrics were able to distinguish significantly (p < 0.01 using Wilcoxon Mann--Whitney test) the different clinical groups considered. An investigation of the anatomical region corresponding to the hubs that have a central role in pathological changes detection, was also carried out.The selected features were used to train another independent RF classifier always with 500 trees. Examples randomly selected to stay out of the training set were then adopted to measure the classification performance on the train subjects in order to evaluate the informative content of the available base of knowledge and detect which regions show an anomalous behavior. This is a useful information for those diseases whose patterns are not yet fully understood. Besides, a comparison of the performances obtained from the SMF, with those obtained from the original multiplex network and from their combination was carried out to deepen the characterization of the salient backbone of the human brain network in the field of the neurodegenerative diseases. Another characterization analysis concerns hub study of the salient network. As hubs are nodes with a number of links that greatly exceeds the average , we consAnother task, in this work, was the assessment of the methodology on the validation subject sets performing a binary classification: normal controls versus MCI converter subjects and normal controls versus Alzheimer’s disease subjects. For validation subjects, features were extracted adding in turn the test subject to the training SMN holding fixed the overall matrix. For each classifier, the tree number was set large enough to reach the typical training plateau for the out-of-bag error. At each split, being f the feature number, The first task is certainly of interest for an assessment of the methodology and the feature informative power. In fact, it is well known which there are brain regions related to AD, thus we can use this classification task to observe whether or not high salience skeleton outlines brain regions coherent with the pathology. The second task is of paramount importance for clinical purposes, in fact especially the MCI condition can be in several cases a prodromal stage of AD. Accordingly, the early and accurate detection of impairment could play a pivotal role in the development of drug trials and therapy developments.There is not any in advance reason for which the scale-free and small-world topologies should have emerged with the same threshold value from different subjects. Nevertheless, Fig. SW averaged over all subjects reported in Fig. Moreover, considering the mean small-worldness indicator Coming back to the original brain network of each subject, we removed the correlations exceeding a threshold value of 0.65 chosen in order to bring out both significantly small-world and scale-free behavior of the networks minimizing variance of the R-adjusted and small-worldness indicators over the subjects. In this way, for each subject, a scale-free weighted network of highly correlated nodes was obtained.First of all, we investigated the informative content of the high salient skeletons to evaluate if it exhibits advantages compared to the complete multiplex network. In the classification of NC versus AD patients, the important features, extracted from the skeleton multiplex network, gave performances, reported in terms of accuracy, area under the receiver-operating-characteristic curve (AUC) and its relative standard deviation, in keeping with the ones obtained using the original multiplex network. In fact, an AUC of In addition, the significant features extracted using the salience approach were not the same obtained with the previous methodology. As a consequence, for assessing if the salient skeletons bring out different relevant information regarding AD disease pattern, the important features, extracted from the two approaches, were combined and used for distinguishing NC and AD. Receiver-operating-characteristic (ROC) curves for the three binary classifications NC versus AD, carried out respectively, using original (OMF), skeleton (SMF) and both multiplex features, were represented in Fig. This study showed that the feature combination makes it possible to achieve an higher classification performance with an AUC of Another adding value given by salient networks relates the study of the hubs. In fact, going from original network to the salient backbone, on average 70 % of the hubs is conserved. Besides, the salient network measures associated to the 15 hubs common to all subjects allowed us to statistically distinguish between NC versus AD and cMCI versus AD with a In Fig. In Fig. To test the reliability of the skeleton multiplex network features (SMF), the classification models obtained during the training phase were used for the validation on the same dataset employed for validating the original networks, two binary classifications: controls vs. AD patients and controls vs. cMCI subjects were carried out.The performances shown in Table In this regard, it is worthwhile to notice, the total amount of CPU time for the whole analysis pipeline based on the salient networks was of 58 h 42 min and 25 s. Therefore, each image processing and the relative subject prediction required a CPU time of 16 min 23 s and 2GB of RAM. This means that the reduction methodology of the salient skeletons provides a relevant computational saving, indeed the time taken to perform the whole analysis with the original networks is about the twice. It is important to specify that we used a single core 2.4 GHz CPU.In this work, it was also studied where are located the significant supervoxels of the salient skeleton. Although the skeleton networks were completely different from the original dense networks and the important features selected were not the same, it is resulted that the significant supervoxels identified anatomical regions associated to the AD progression in keeping with the literature. In Fig. It is worthwhile to note as significant supervoxels covered regions such as hippocampus, amygdala, ventricles, thalamus, brain stem, cerebral sulci, inter-hemispheric portions and separation areas between gray and white matter. These findings are a further confirm that salient skeletons are an alternative way of detecting AD patterns, able to give supplemental information regarding the disease.The proposed approach aims at reducing dense and large brain networks preserving and underlining the meaningful information. In fact, nowadays especially in the neuroscience field, the necessity to perform multimodal longitudinal and even multi-scale studies has been increasing more and more and the management of large and numerous dataset has been becoming a really challenging task. Methodology includes two reduction steps. The first concerns the threshold study to extract the two fundamental topologies of the brain networks. This investigation provides two strategical aspects. On one hand, it confirms human structural brain networks analogously to the functional ones  can be dIn this article, we proposed a novel method for reducing complexity of dense and large brain networks, keeping and especially highlighting, their informative content. A threshold analysis was performed to bring out the two main topologies  of the brain networks. In fact, the most of the behaviors of integration and segregation involved in brain organization are explained by these topologies. We also examined how a neurodegenerative disease, such as Alzheimer, can change structural connectivity of these brain networks extracting high salient skeleton for each one, in order to turn particular attention to the hubs and highways of these correlation networks which could be damaged by AD.We assessed the informative power of the high salient networks through a multiplex network approach. Feature extracted from the skeleton multiplex network give in cross-validation, for the distinction of AD subjects from normal controls, an accuracy of


Background Insight into the delivery of interventions is necessary to gain a better understanding of what caused an intervention to succeed or fail. The Cardiovascular medication non-Adherence Tailored Intervention (CATI) study failed to show effectiveness of a patient-tailored, pharmacist-led intervention programme on self-reported adherence to antihypertensive medication. Objective To evaluate the implementation fidelity of the CATI intervention programme. Setting Twenty Dutch community pharmacies. Method The process of a randomised controlled trial was evaluated. Both quantitative and qualitative data were collected and analysed according to Carrolls’ Conceptual Framework for Implementation Fidelity. Implementation fidelity is defined as the degree to which the intervention was implemented as intended. Main outcome measure Four key intervention components of the intervention programme . Results For most participants the key intervention components were implemented as intended. The training of pharmacists, intensive monitoring during the study and structured and easy-to-use intervention materials facilitated the implementation of the intervention. The method to select participants for the intervention programme was considered insufficient and pharmacists questioned the eligibility of some participants because of a low degree of intake non-adherence. Conclusion Implementation fidelity was moderate to high for all key intervention components. Therefore, the absence of effectiveness of the CATI intervention programme on self-reported medication adherence cannot be explained by poor implementation of the intervention. However, the limited genuine eligibility of some participants resulted in a limited potential for improvement in medication adherence. Pharmacist-led consultations to discuss patients’ medication adherence and barriers to adhere to medication seem feasible.The challenge remains to identify a patient group that is eligible for adherence enhancing interventions.Extensive communication skills training, easy-to-use and system integrated intervention materials and sufficient time seem necessary to implement adherence enhancing interventions in daily practice.In the last few decades, multiple pharmacist-led interventions have been developed and investigated with respect to their effectiveness in improving medication adherence. Unfortunately, these studies showed inconsistent and disappointing results –4. InterOne way to gain insight into the way an intervention is delivered is through the assessment of implementation fidelity, defined as the degree to which an intervention is implemented as intended by the developers , 6. ImplThe Cardiovascular medication non-Adherence Tailored Intervention (CATI) study, a pragmatic randomised controlled trial (RCT) with 9 months of follow up, has recently been carried out to investigate the effectiveness of a patient-tailored, pharmacist-led intervention programme on self-reported medication adherence in 20 community pharmacies . In shorThe present study aims to evaluate the implementation fidelity and potential moderating factors that might have influenced the implementation of a patient-tailored, community pharmacist-led intervention programme to enhance adherence to antihypertensive medication.All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The Medical Ethics Committee of the VU University Medical Center Amsterdam approved this study (no. 2015/219). Informed consent was obtained from all individual participants included in this study.The study design and methods of the CATI study have been described in more details elsewhere . A flow adherence to the intervention, defined as the degree to which the intervention has been delivered as intended by developers. Adherence can be operationalised by the following categories: coverage, content, frequency and duration. Coverage refers to the proportion of participants exposed to the intervention as intended. Content refers to what extent components of the intervention were delivered as planned. Frequency and duration refer to the delivery of the intervention at the intended intensity. In this framework, potential moderating factors, which might influence the implementation process and as such the level of fidelity, must be considered. The four potential moderating factors are: intervention complexity, facilitation strategies, quality of delivery and participant responsiveness [To assess the implementation fidelity, a modified version of the conceptual framework proposed by Carroll et al.  was usedsiveness . BrieflyA.First consultation—barrier identificationDuring the first consultation, participants’ barriers to adhere to medication should be identified by means of the Quick Barrier Scan (QBS), which consists of 11 questions representing various barriers, such as lack of knowledge, forgetfulness and side effects. In addition, one open-ended question explores potential other barriers according to the participant .B.First consultation—information & adviceBased upon the barriers identified, at least one out of five corresponding intervention modules (IM) should be selected from the Tailored Intervention Guide (TIG) . This guC.First consultation—written summaryAt the end of the first consultation, the participant should be handed a written summary of the consultation, including both the information and recommendations provided.D.Follow-up consultationDuring the follow-up consultation, participants’ implementation of and experiences with the information and recommendations provided previously should be discussed according to protocol instructions.For the evaluation of the implementation fidelity of the intervention programme, four key intervention components were identified by the researchers. These key intervention components included:Specific research questions per key intervention component for the different elements of the conceptual framework have been presented in Tables To answer the specific research questions, the following data sources, numbered with roman numerals (I–V), were used.The researcher kept administrative records (I) on the logistics and process of the implementation of the intervention programme. The researcher recorded data on the flow of participants throughout the study.Pharmacists filled out an administration form (II) for each participant, including a description of the pharmacists’ impression of both the first and follow-up consultation and data on time investment (in minutes) per consultation. In addition, during the first consultation, the pharmacists used intervention materials (III), from which data concerning identified barriers and provided information and recommendations were retrieved. Semi-structured interviews with all participating pharmacists (IV) were conducted at the end of the study. The following topics were included: eligibility of included participants, impression of the first and follow-up consultations, usability of intervention materials, feasibility of implementing the intervention programme in daily practice and advantages and disadvantages of the intervention programme.Participants from the intervention group filled out an evaluation questionnaire (V) in order to assess their reasons for participation and need for help prior to the study and their experiences and satisfaction with the different components of the intervention programme. This questionnaire was self-composed by the researchers and consisted of several closed-ended questions rated on a Likert scale . In addition, using a small number of open-ended questions, participants were invited to describe their impression of the first and follow-up consultations in more detail.Quantitative data were presented as frequencies with percentages. For the quantitative data that were collected by the evaluation questionnaire, only results of participants with complete data were reported. For the qualitative data collection, semi-structured interviews were audio taped and transcribed. The transcripts were analysed using the Framework approach . Two indDetails on the outcomes per key intervention component for each aspect of the conceptual framework are presented in Tables In order to include a sufficient number of participants and reach the pre-calculated sample size, seven additional pharmacies were recruited, resulting in a total of 20 participating pharmacies. Of 1338 selected patients from 20 community pharmacies, 170 patients (12.7%) were eligible to participate, of which 85 were randomised to the intervention group and 85 to the control group. According to the participants’ evaluation questionnaires, the most reported reason to participate in this study was contributing to scientific research. Patients were included if they were non-adherent, according to both pharmacy dispensing data (refill non-adherence) and a self-report questionnaire (intake non-adherence). In the semi-structured interviews some pharmacists indicated that the non-adherence classification according to refill data for some participants might have been caused by missing dispensing data, which resulted in misclassification of these participants. In addition, pharmacists perceived the degree of intake non-adherence in some participants so minimal that they assumed limited potential for improvements in medication adherence. Pharmacists also indicated that some participants did not seem eligible for the intervention, because of the limited number of prescribed medicines and the lack of structural difficulties with medicine intake. When comparing the implementation of the selection procedure with the study protocol , both meBased on the filled out intervention materials, the identification of participants’ barriers to adhere to medication by means of the QBS was delivered as planned. For nearly all participants, one or more barriers were identified. Subsequently, pharmacists provided information and advice using the correct corresponding intervention module of the TIG to almost all participants. In the semi-structured interviews, pharmacists indicated that for certain participants they did not perceive the identified barrier as being causative for the medication non-adherence and therefore decided to provide only general information about hypertension. Providing a written summary for participants to take home was carried out as planned for almost two-thirds of the participants. The discussion of participants’ experiences with the information and advice provided at the first consultation seems to have been implemented as planned, based on the descriptions of the follow-up consultation in pharmacists’ administration forms. However, data concerning which recommendations were actually implemented by participants has not fully been reported objectively. Nevertheless, pharmacists indicated that about half of the participants appeared to have acted upon the advice provided at the first consultation, as they displayed an improvement in adherence-related beliefs or behaviour.For 75 out of 85 intervention participants, a first consultation was performed. Reasons for not performing this consultation were mainly related to logistic and time management problems of pharmacists. For 66 participants, a follow-up consultation was performed. At the first consultation, an average of two barriers were identified per participant Table . No barrAt the semi-structured interviews, the majority of pharmacists evaluated the protocol description of the first consultation as clear and informative. Some pharmacists indicated that it was extensive and too time-consuming. Nearly all pharmacists indicated that the intervention materials were clear and easy to use. After identifying a barrier, pharmacists found themselves easily guided to the corresponding intervention module to provide participants with tailored information and advice. For participants that seemed less eligible for the intervention, pharmacists indicated that it was difficult to use the intervention materials according to protocol. Some pharmacists found the protocol description of the follow-up consultation somewhat ambiguous. Therefore, all pharmacists received additional protocol instructions during the study.A 1-day training session, a structured protocol, intermediate instructions, intensive monitoring and financial incentives were provided to facilitate the implementation of both consultations by pharmacists. Seven pharmacists did not attend the 1-day training session, of which four were unable to attend that specific day and three had not yet been included in the study. The pharmacists who did not attend the training session received individually received additional instructions at the start of the study. Attending pharmacists rated the training session useful as a preparation for study participation. According to the pre-post assessment, the majority found to have improved their knowledge and competences at the training . Three pAccording to pharmacists, the intervention materials available for the first consultation were of high quality. At the semi-structured, pharmacists indicated that they could easily identify barriers for adherence for most participants and inform and advise them accordingly. Almost one-third of pharmacists did not see the need for making a written summary at the end of the first consultation. Pharmacists indicated that the follow-up consultation was important for most participants to monitor the feasibility of the advice provided previously. As assessed by the evaluation questionnaire, most participants rated the consultations with the pharmacists as pleasant. Participants also indicated appreciation and satisfaction for the personal attention and provided support from pharmacists. Half of the participants indicated the follow-up consultations were of added value, and nearly all participants would recommend participating in these consultations to others patients.As assessed by the evaluation questionnaire, about one-third of participants indicated to be in considerable need for help at the start of the study. A few participants indicated that difficulties with the use of their medication had a substantial negative influence on their daily life. Despite a limited proportion of participants indicated a need for help, the attendance rates of the first and follow-up consultations were quite high. In the evaluation questionnaire, the majority of participants indicated that the information and advice was helpful and allowed them to better cope with difficulties regarding medication intake. With regard to the responsiveness of participating pharmacists, the majority of the pharmacists were well engaged to the intervention. However, two pharmacists had difficulty with implementation of the intervention and required frequent monitoring. One pharmacist devoted the execution of the intervention to a pharmacy technician. At the semi-structured interviews, most pharmacists indicated that the intervention would be useful for supporting patients with serious adherence problems in daily practice and that they would like to perform similar consultations with their patients in the future.Additional information regarding the feasibility of the CATI intervention programme, beyond the scope of the conceptual framework for implementation fidelity, was obtained from the semi-structured interviews conducted with the pharmacists. Most pharmacists indicated that they believed the intervention programme to be feasible in daily practice. However, they emphasised that it would be better to focus on smaller groups of patients. In addition, the majority of pharmacists indicated the benefit of asking for the assistance of a pharmacy technician for logistics and administration of the programme, and some pharmacists believed that a pharmacy technician would be able to execute the programme, following appropriate communication and consulting training. A few pharmacists indicated that the current design of the intervention programme was not feasible in practice, since the consultations are very time consuming. Suggested changes were to replace the face-to-face consultation with a consultation per telephone. From the viewpoint of logistics, this would save time and allow for more participants to be reached. Pharmacists also indicated that they preferred the intervention materials to be integrated in their pharmacy information systems which would allow to intervene with system-identified non-adherent patients when they visit the pharmacy for a refill. Some pharmacists also suggested that it would be beneficial to expand clinical medication reviews with adherence-enhancing consultations.The present study aimed to evaluate the implementation fidelity of a patient-tailored, community pharmacist-led intervention programme to enhance adherence to antihypertensive medication. According to the rating of the researchers, the implementation fidelity was moderate to high for all key intervention components, meaning that these components were mostly carried out as planned. However, the method to select patients was considered insufficient, and pharmacists questioned the eligibility for the intervention of some participants. Moreover, we cannot rule out selection bias, since large numbers of patients did not respond or declined to participate.The implementation fidelity of the four key intervention components was evaluated. The first consultation was carried out as planned. The identification of participants’ barriers to adhere to medication as prescribed and the provision of tailored information and advice were well implemented for most participants. However, one-third of the participants was not given a written summary at the end of the first consultation. Despite protocol instructions, some pharmacists did not see the necessity of the written summary. Therefore, it appears that the instructions were not sufficiently clear or that the importance of this step had not sufficiently emphasised. At last, the limited eligibility of some participants made it difficult to use the intervention materials properly, thereby hindering its implementation for a small proportion of participants.The training of pharmacists, intensive monitoring during the study and the structured and easy-to-use intervention materials were found to have facilitated the implementation of the intervention. Pharmacists also appreciated the financial incentive, however, they indicated that a large-scale implementation in daily practice would require higher reimbursement due to the time required to conduct the intervention.Since the intervention programme was mostly developed based on our previous research, comparable intervention studies in the literature are scarce. Two previous studies with quite comparable interventions reported on the effectiveness of the intervention but did not perform a process evaluation , 19. ThiA strength of our process evaluation is that it provides insight into the delivery of an intervention, including whether the lack of effectiveness of the intervention was due to poor implementation . MoreoveThe implementation fidelity was evaluated alongside a pragmatic randomised controlled trial, which does not fully resemble daily practice. The researcher selected patients in each pharmacy, sent invitations for participation and reviewed the filled-out self-reported questionnaires for eligible patients. Preferably by using the pharmacy information system, community pharmacists or pharmacy technicians must be instructed on how to select eligible patients for adherence-enhancing interventions in order to allow the successful implementation of this multicomponent intervention in daily practice. Moreover, more insight is needed into which methods can be used to properly select non-adherent patients as such. Performing the consultations by a community pharmacist appears to be feasible in practice. However, with respect to logistics and administration the involvement of a pharmacy technician would be helpful. A recent review concluded that pharmacy technicians are a valuable asset to community pharmacists in the process of implementing and operation of adherence programmes . The amoIn addition to the changes suggested to allow successful implementation of the present intervention programme in daily practice, some general factors relevant to fidelity of pharmaceutical care implementation in community pharmacies should be considered. Alongside strong methodological designs of intervention studies, including a tailored approach and a theoretical framework, it is important to consider factors like adaptability, context and climate, logistics support by staff and sustainability of the community pharmacy setting , 13, 24.In this process evaluation, nearly all key intervention components were carried out as planned. Therefore, the absence of effectiveness of the intervention programme on self-reported medication adherence cannot be explained by a poor implementation of the intervention. However, the possibility of a selection bias and the questionable eligibility of certain participants, mainly due to a rather low degree of intake non-adherence, appeared to have resulted in a limited potential for improvement of medication adherence. Extensive communication skills training, easy-to-use and system-integrated intervention materials, appropriate time and resource allocation and genuinely eligible patients appear to be necessary elements for successfully implementing adherence-enhancing interventions in daily practice.


The chick chorioallantoic membrane (CAM) is an extra-embryonic membrane, comprised of a high density of blood and lymphatic vessels. CAM has a dense capillary network and is commonly used to study in vivo angiogenesis and anti-angiogenesis in response to potential biomolecules and drugs. Most of the earlier reported CAM assays described the in-ovo method—where the viability of the embryo is higher, but accessibility to the CAM is limited. Ex-ovo CAM methods were previously described that employed shell-less cultures of chick embryos, but the low viability of embryos reduced the overall robustness of the angiogenesis assays. We described a method (named as cup-CAM method) which is more economical, has better accessibility and has significantly improved the viability of the embryo till advanced developmental stages. We could perform this simple yet useful experimentation with the common tools available in the laboratory. We successfully used the cup-CAM method for showing the paracrine effects of conditioned media from tumor cells, on the angiogenesis. This method can be used to assay the angiogenic potential of a drug or protein and to observe the embryonic development of the chick embryo and other related scientific applications. The phenomenon of the formation of new blood vessels from pre-existing ones, known as angiogenesis, is a crucial process involved in many physiological activities, such as embryonic development, wound healing, inflammation and organogenesis . Tumor gVarious animal model systems were developed, ranging from zebrafish to mice, to study the process of angiogenesis and to observe the effects of pro-angiogenic and anti-angiogenic factors ,4,5,6,7.We explained in detail through this protocol, a cost-effective version of ex-ovo CAM assay—which we carried out employing simple facilities available at a research laboratory—with high efficiency.The ex-ovo CAM assay exploited the highly vascular chorioallantoic membrane to understand the angiogenic potency of various biomolecules or drugs. The ex-ovo assays were conducted by culturing the egg components, i.e., embryo, yolk and albumin outside the egg shell from the 3rd embryonic day onwards. After culturing the embryo until the 7th embryonic day, a disc soaked with the test component was placed on the region where two major blood vessels bifurcate. Upon impregnation of the disc, the embryo was grown till the 13th embryonic day. On the 13th embryonic day, the images of the region containing the disc on the CAM were taken with the help of a stereomicroscope. The complete CAM image was also taken with the help of a charge-coupled device (CCD) Camera to aid in the final quantification of the angiogenesis. We quantified this assay by manually counting the number of blood vessels arising centrifugally from the discs—conducted by the experimenter and two unbiased observers—independently. We found this process to be a simple method that can be used in future experiments to assay the angiogenic potential of a drug or biomolecule and to observe the embryonic development of the chick embryo and other related scientific applications.3-day old chicken embryonated eggs .Wheat husks, for maintaining temperature and humidity for the egg during transportation.Alcohol .An egg-tray, for holding the eggs before culturing in the cups.Plastic cups (better if transparent).A cuboid metal bar, for cracking the eggshell.Scissors, for cutting the eggshell after cracking—to ensure proper transfer of embryonic contents into the cups and for cutting the cling wrap.Cling wrap, for covering the shell-less culture.Rubber bands, for holding the cling wrap on the plastic cups.Toothpicks, for making pores on the cling wrap to provide ventilation for the embryo.Whatman filter papers (thickness 1 mm), for making circular discs impregnated over the CAM.Forceps, for dropping the discs over the CAM.Dulbecco’s Modified Eagle Medium .Cell culture Dishes .2 Incubator .A COA stereomicroscope  Ltd., Milton Keynes, UK).A Charge-coupled Device (CCD) Camera .Herein, we describe the detailed procedure to carry out the cup-CAM assay. All the experiments should be done in germ-free conditions of a cell culture facility to maintain sterility. The outer surfaces of the eggs need to be cleaned with distilled water instead of alcohol, as alcohol has been reported to increase the embryonic mortality. The experimental steps need to be carried out in laminar flow hood, thus maintaining sterility. Sanitization of all of the equipment with 70% alcohol prior to the experiments should be done.3-day old specific pathogen-free embryonated eggs should be obtained from the hatchery and kept inside the husks during transportation to the lab. The presence of an embryo and its location inside the eggs should be checked using an egg candler.The eggs should be placed horizontally in a suitable egg tray and incubated for an hour at 37 °C and 50% humidity without rotation, inside an incubator. The egg tray containing the eggs should be taken out from the incubator and kept inside a laminar flow hood.The eggs should be kept in a horizontal position and cracked open using the moderately sharp edge of a sterilized hard cube/rectangle shaped structure, lying perpendicular to the horizontal axis of the eggs. The cracked eggs should be placed just above the surface of the cups to avoid any leakage of the egg white. The contents of the eggs should be transferred into the cup in a continuous flow by carefully applying the pressure on the crack—without breaking the connection of the inner and the outer contents of the egg or damaging the embryo and the vessels around it. The cups should be covered with sterile cling wrap with the help of rubber bands. Small pores should be made on the wrap with the help of sterilized toothpicks to provide ventilation for the chick embryo inside the cup. The ex-ovo cultures should be kept back in the incubator for the next 96 h and should be maintained at 37 °C and 50% humidity. (Note: We used cell culture incubators for incubating the eggs. We maintained the humidity by keeping a water tray at the bottom of the incubator.)The tumor cells—of interest from all experimental groups, should be cultured in complete media.To prepare the conditioned media, equal number of cells should be plated in 60 or 100 mm dishes for all experimental groups.Upon reaching 50–70% confluency, the complete medium from the dishes should be replaced with low-serum or serum-free DMEM (Dulbecco’s Modified Eagle’s Medium). (Note: The amount of serum for each cell line needed optimization.)At 24–72 h post incubation, the conditioned media should be collected from the dishes and centrifuged at 4000 rpm for 3 min. The conditioned media can be snap-chilled prior to use for later experiments or lyophilization. Circular discs can be created from filter papers  using a hole-puncher. The discs should be autoclaved and dried completely before using them as carriers for the test substances to the CAM.These circular discs should be soaked with equal volumes of the conditioned media from all experimental groups used for the angiogenic studies. (Note: The dilutions may vary according to the experiments.)After keeping the ex-ovo culture back for 96 h Images of the embryo along with the discs should be taken for the end-point analysis of the CAM assay.The cultures should be re-covered with the same cling wrap and maintained in the incubator—in conditions mentioned in step 7 of The cups containing the embryos should be taken out of the incubator carefully after 120 h of incubation and then placed under a stereomicroscope/CCD (Charge-coupled Device) camera (in-situ imaging).The image of the region surrounding the disc should be captured using a stereomicroscope (8× magnification) and the image of the entire embryo using a CCD digital camera. The images obtained from the stereomicroscope can be processed for background corrections using imaging software  and the manual counting of the microvessels arising centrifugally from the disc can be done.  in each experimental group.)t-test—plotted with the help of GraphPad Prism.The number of micro vessels obtained from each group can be compared using the unpaired We performed two types of experiments, one with previously reported ex-ovo and in-ovo methods and another with our improvised cup-CAM method. We kept the growing conditions the same for all the sets and checked the survivability in each experimental group over the whole experimental period. Apart from survivability, we took accessibility and ease of handling into consideration and assessed the overall efficiency of all the setups.While trying to replicate previously known in-ovo methods A, we reaThe ex-ovo setups required minimal handling expertise and were easier to replicate. However, in these setups, the embryo mortality (70–80%) arose because of the container used. In the case of Petri dishes B as the Representative pictures of key steps of the cup-CAM method are shown in We found it interesting that we obtained only 5–10% embryo mortality in the ex-ovo method using our preferred approach with the plastic cups. The plastic cups had the right set of dimensions  to support proper embryonic growth. The lesser diameter provided appropriate conditions, and hence solved the problem of rupturing due to higher surface tensions. There was also no vigorous movement of the egg content, as was observed in the glasses. Hence the experimental disc placed did not sink nor became displaced in the egg content. Surface accessibility was pretty high and thus was perfect to manipulate various regions of the CAM. By combining all of these factors, this improvised cup-CAM method was found to have higher survivability compared to other methods , full suThe CAM assay was found to be a very powerful tool to study the angiogenic factors and their role in the angiogenesis. However, the CAM assays require the eggs to grow outside their natural environment, which causes a major restriction. The development of the in-ovo assay was thought to solve this problem. The in-ovo assays presented with other problems. The dropping of the CAM in this protocol by the application of a pressure pump seemed difficult, as it required much expertise and thus led to lower survivability in our experimental setup. The lack of accessibility of the CAM in the in-ovo method added to the lower efficiency of the in-ovo CAM assay.The lower efficiency of the in-ovo setups and well-known importance of the CAM assay for experimental purposes boosted the development of ex-ovo setups. These setups had high accessibility and easier mode of experimentation, but the embryonic survivability was severely compromised. We found that the lower efficiencies of different ex-ovo setups such as Petri plates is due to the wider base of the plates that results in the spreading of the egg contents—which then leads to rupture and leakage of the yolk. The usage of plastic Petri plates gave rise to an inexpensive ex-ovo CAM assay, but the lower embryo survivability issues demanded the use of a very high number of embryonated eggs. We can attribute lower efficiency of ex-ovo setup in glasses to the movements of the yolk—which due to the narrowness and the depth of the vessel, pushes the testing disks towards the periphery of the vessel. The disks on the periphery can indicate factors about the vessels arising towards the center but fail to provide information on the vessels towards the periphery and hence, make the study meaningless. Our use of the plastic glasses method seemed cost-effective. However, the low viability—as well as the difficulty in analysis due to peripheral movement of the test substance—led to our search for other effective CAM assays.We found interesting the higher efficiency and viability of ex-ovo setup by growing the chick embryo on plastic cups covered with transparent wraps accompanied by small pores for ventilation. The depth and width of the cups were optimal for gaining higher efficiencies along with slightly modified growth and transport conditions . We can attribute the low embryonic death (5–10%) observed in the cup-CAM assay to the deviation from the absolute natural embryonic growth conditions, including required absolute temperature and humidity provided by the mother. As mentioned earlier, the materials used were easily affordable and consequently made it a very feasible CAM assay to study the angiogenesis process.In conclusion, with the embryonic survivability rate as high as the natural conditions and the ease of experimentation and analysis, this new protocol merges the advantageous qualities of both in-ovo and ex-ovo setups. The method provides an overall highly efficient and cost-effective way to study the phenomenon of the angiogenesis regarding the basic mechanism and drugs for angiogenesis-based therapies.We have described a unique and robust method for performing chick ex-ovo CAM assay as a tool for investigating pro and anti-angiogenic agents in vivo. This protocol (cup-CAM), in combination with basic laboratory facilities and low-cost materials, provides the system to conduct a highly efficient angiogenesis assay.We thawed DMEM, mixed with amphotericin and filtered through 0.22 um filter and kept the mixture at 4 °C for up to 2 weeks.