Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB , and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage:  Magneto/Electroencephalography (M/EEG) are key techniques to analyze functional connectivity from surface signals , 2 or/anSo far, approaches based on graph theory have represented brain networks as sets of nodes interconnected by edges . Once thThe graph theory based analysis has been widely used to characterize normal  and pathIn the M/EEG context, nodes represent either the electrodes or the dipole sources depending on whether the connectivity is analyzed at scalp or at reconstructed source level, respectively. The edges are defined by the values of the statistical dependencies among M/EEG signals or among reconstructed time courses of cortical sources.On the one hand, several tools were developed to process M/EEG signals such as EEGLAB , CARTOOLAll these packages are typically specialized for processing a particular step in the whole pipeline aimed to identifying and characterizing brain networks. However, a tool that comprises the complete pipeline from M/EEG processing to analysis/visualization of brain networks is still missing. This consideration led us to develop and present EEGNET, MATLAB-based software with Graphical User Interface (GUI). Our main objective was to develop a complete framework that can cover most of the processing from EEG recordings to graph analysis and visualization. This pipeline includes: 1) loading and filtering the M/EEG signals, 2) the solution to the inverse problem and the reconstruction of the cortical sources, 3) the computation of the functional connectivity, 4) the calculation of the network measures and 5) the visualization of 2D  and 3D (cortex level) brain networks and associated measures.EEGNET is a useful processing pipeline to identify, visualize and characterize brain networks from M/EEG recordings. It can perform all steps including the estimation of brain sources, the computation of the functional connectivity and the mapping of brain networks at scalp level and/or at source level. The basic workflow is shown in The main elements of EEGNET are:Nc x Ns x Nt) where Nc, Ns and Nt are the channels (brain regions in the case of sources file) number, the sample size and the number of trials , respectively. When solving the inverse problem and for visualizing the network at scalp level, the electrode location file is required. In the current EEGNET version, both the.xyz and.mat formats are supported.This file represents either the scalp EEG data or the reconstructed sources. The default file format is the ‘.mat’. It should be a 3 dimensional matrix  linear filter before computing the function connectivity (FC) matrices. This feature allows users to choose the frequency band where to compute the FC. The data can also resampled by changing the sampling frequency. This feature can be essential in some cases where users attempt to reduce the size of the data. The preprocessing tool allows user also to specify the baseline from the visualized data. This baseline is essential for computing/normalizing the post vs. pre stimulus connectivity for instance or to compute the noise covariance matrix when solving the inverse problem.EEGNET provides also the possibility of computing the time-frequency representation of the data. In the current version, the complex Morlet wavelet is used as it was shown to provide a good compromise between time and frequency resolution –42. ThisNc x Nc dimension. It contains the values of the functional connections between all the channels (or brain regions). This file can be also in Ns x Nc x Nc in the case where it is the dynamics of functional networks that is being analyzed. To compute the functional connectivity (FC) matrices, four methods are available: the cross-correlation, the mean phase coherence (MPC), the mutual information (MI) and the Phase Locking Value (PLV), see  is sorted in increasing order and the rank index for Corg is returned. With a number of surrogates (nsurr = 100 for example), if this rank is > 95 and < 5 (significance level at 95%), this means that it lies in the tail of the distribution, and that the null hypothesis can be rejected (two-tailed test) with a significance of p = 2*(1/ (nsurr+1)) = 0.019. The output of this analysis is the matrix containing only the significant connections.To ensure the significance of the obtained FC matrices, we integrated a statistical test based on the surrogate data analysis. Briefly, we use multivariate Fourier transform surrogates generated from the original EEG data. Such surrogates correspond to realizations of linear stationary process with conserved auto-and cross-correlation characteristics. The null hypothesis is tested by comparing the original connectivity value . This file contains the labels of all the ROIs based on the already used atlas for segmentation such as Desikan [When realizing M/EEG source connectivity, the cortical surface file and its corresponding scout file are required. The surface file contains the cortical mesh and the scout file contains the labels of the brain regions in case of using specific atlas. The cortical parcellation provides the ROIs that are used as network nodes in EEGNET. The surface files store geometric information about the morphology of the cortex. It can be created using the open source imaging analysis tool FreeSurfer  Desikan  and Dest Desikan .To characterize the obtained networks, graph theory based analysis was widely used and proved its high performance and usefulness . A graphGlobal featuresA. Density: the density of a graph is the fraction of present edges to all possible connections. If the density of a graph is 1 then it is a complete graph (every vertex is connected to every other vertex)- characteristic path length: is the average shortest path lengths in the network- The global efficiency is the average inverse shortest path length in the network- The Radius: The radius of a graph is the minimum graph eccentricity of any graph vertex in a graph. A disconnected graph therefore has infinite radius. Eccentricity is the maximum graph distance between a vertex v and any other vertex u of the graph.- Diameter: graph's diameter is the largest number of vertices which must be traversed in order to travel from one vertex to another.- Node parametersB. Degree: Node degree is the number of links connected to the node. In case of directed graph, the indegree is the number of inward links and the outdegree is the number of outward links, and the total degree is the sum of both indegree and outdegree.- Clustering Coefficient: The clustering coefficient is the fraction of triangles around a node i.e. the fraction of node’s neighbors that are neighbors of each other.- K-Coreness Coefficient: The k-core is the largest subgraph comprising nodes of degree at least k. The coreness of a node is k if the node belongs to the k-core but not to the (k+1)-core.- Node Betweenness: Node betweenness centrality is the fraction of all shortest paths in the network that contain a given node. Nodes with high values of betweenness centrality participate in a large number of shortest paths.- Participation Coefficient: Compares the number of links (degree) of node i to nodes in all clusters with its number of links within its own cluster. The participation coefficient plays an important role in classifying the nodes of a graph as connector hubs and provincial hubs.- Edge parametersC. Edge Betweenness: Edge betweenness centrality is the fraction of all shortest paths in the network that contain a given edge. Edges with high values of betweenness centrality participate in a large number of shortest paths.- Shortcut Edges: Shortcuts are central edges which significantly reduce the characteristic path length in the network.- Edges Neighborhood Overlap: is the number of nodes that are neighbors of the nodes of that edge.- See  for detax, y and z positions (an example of channel location file is contained within in the Examples folder included in the downloaded EEGNET). This part supports the static and dynamic option. The static option requires a 2D matrix (Nc x Nc) while the dynamic behavior option requires 3D matrices (Ns x Nc x Nc). Typical examples of the static and dynamic scalp networks are presented in When the user is only interested in scalp networks, the EEG data should be firstly loaded. The functional connectivity is then computed among scalp signals, according to a pairwise procedure. For visualizing the network and computing the network measures, the channel file is required. Users can also directly import their connectivity matrices (computed elsewhere) to compute the network measures and visualizing the network. The channels position is a file with four columns, the first for the node number or label, the next three for To compute the brain networks at source level from M/EEG data, the inverse problem must be solved. It consists of reconstructing the brain sources from the scalp M/EEG. When the M/EEG signals are checked and approved for further analysis, the time series of the reconstructed sources can be estimated. After loading the coordinates of the electrodes as well as a brain surface mesh, the lead field matrix can be computed using different tools such as ‘OpenMEEG’ . The timExamples folder for an example of source level matrix). Once the connectivity matrices are obtained and loaded, a set of measures can be extracted from these matrices. We integrated a number of network measures developed in the BCT toolbox [k-means clustering tool of the functional connectivity [This step can be performed within EEGNET or elsewhere (in Brainstorm for instance). The file containing the time series of the sources can be directly loaded as input for the next step, which consists in computing the functional connectivity, see  for comp toolbox . EEGNET ectivity .First, An essential feature of EEGNET is the possibility of quantifying the obtained networks. The network measures computed from the BCT toolbox can be visualized in a quantitative way as shown in The quantification can be realized in different level. It can show the averaged values over the brain lobe , the averaged values over the ROIs segmented from a given Atlas such as the 148 of Destrieux Atlas or/and the values of each subdivided ROI as shown in In this section, we show the difference steps realized using EEGNET to identify networks involved during picture naming task for a given subject. Participant was asked to name 148 displayed pictures on a screen. The brain activity was recorded using dense-EEG, 256 electrodes, system . EEG signals were collected with a 1 kHz sampling frequency. After loading the signal, to obtain the scalp level network, the functional connectivity was computed using PLV method at gamma band (30–45 Hz), The source level network at the same period is shown in EEGNET was developed using MATLAB as programming language with a user-friendly GUI under 64 bit Windows 7 environment, this toolbox has been successfully tested on different operating systems with MATLAB installed, including Windows 7, Linux and Mac OS under 64-bit versions. To facilitate the first use of EEGNET, tutorial and user manual documents are available in the download webpage that also provides the user with some examples for scalp and source networks. It is worth mentioning that EEGNET depends on other software tools. Some of these tools are written in MATLAB such as the BCT toolbox . PreferaEEGNET is licensed under the GNU General Public License version 1. This is a free software license, such that EEGNET may be freely redistributed and modified by any party. However, when distributing the software, the imposition of any restrictions on any further redistribution is forbidden.conneXion study, agreement number (2012-A01227-36), promoter: Rennes University Hospital, Rennes, France. Participants provided their written informed consent to participate in the study.The sample data used in the paper was approved by the National Ethics Committee for the Protection of Persons (CPP), To identify networks from M/EEG data, at least four tools are required from loading/preprocessing the EEG data, solving the inverse problem, computing the functional connectivity, computing the network measures to then visualizing the identified networks in interactive way. However, researchers always look for reducing the number of tools they use to accomplish a complete data processing. As a network identification/visualization tool, EEGNET achieves most of these functions.In addition, the interactive analysis/visualization is a crucial part of scientific research. The easy visualization of data can inspire novel hypotheses, help researchers to quickly evaluate their results, and allow for significant quality control. EEGNET has novel interactive visualization features not available in existing software packages for visualization of the connectome such as the ability to interactively threshold networks based on the network measures for instance.EEGNET provides all the steps from loading the EEG signals to the identification of the brain networks. However different files are needed to accomplish these steps. Here we show some suggestions to how obtain these files.This file is very crucial to the analysis/visualization of the cortex level networks. Using the structural MRI of the participant (or template), FreeSurfer  can be uhttp://home.kpn.nl/stam7883/brainwave.html) and the MATLAB toolbox for FC [The functional connectome is characterized by statistical independences between neural activities in different regions. In the M/EEG context, the FC is usually computed between signals recorded at the scalp signals using different methods such as cross-correlation , 56, phax for FC  are openIn fact, EEGNET makes use of one third-party toolbox, namely the Brain Connectivity Toolbox (BCT). BCT provides tools for network analysis based on graph theory; It was interfaced with EEGNET such that measures on networks can be immediately computed without any interaction with the BCT codes. However EEGNET goes beyond the sole computation, as we added a unique feature allowing the user to visualize the BCT measures as “intuitive” graphical features related to network nodes (for instance the sphere radius/color) and edges (for instance the thickness/color).EEGNET and the exiting M/EEG toolboxes (such as EEGLAB or Brainstorm) respond to distinct (and somewhat complementary) objectives. The main originality of EEGNET is to provide functional connectivity measures , 2D/3D connectivity visualization nor network measures . In the latest version of brainstorm, some connectivity measures  were included. However, it does not implement EEG scalp connectivity visualization or EEG source connectivity 3D visualization. Moreover, network measures were not included in brainstorm.Freesurfer is open-source software widely used by the Neuroimaging community. It was not integrated with EEGNET but can be optionally used to get the cortical surface from MRI in the case where subject-specific data is necessary. The corollary is that users interested in scalp EEG connectivity do not need to use Freesurfer. Users interested in the EEG connectivity at the source level will need to load their own cortical surface or use the template provided in EEGNET.eConnectome  is the cA crucial difference between eConnectome and EEGNET is that EEGNET offers the possibility of computing the network measures  directly on the graph. This step became crucial when analyzing functional brain networks . With EEThe two tools include totally different family of connectivity measures. The current version of EEGNET provides four different functional connectivity measures, the cross-correlation, the mean phase coherence (MPC), the mutual information (MI) and the Phase Locking Value (PLV) while eConnectome offers methods of functional connectivity based on the multivariate autoregressive mode (mainly Directed transfer function DTF and adaptive DTF).EEGNET also provides the possibility of visualizing the network based on the computed metrics. Therefore, the visualization is very different between eConnectome and EEGNET. In EEGNET, the nodes and edges can encode the network measures providing the user with direct and intuitive representation of graph features. For instance, the node size and color can be changed to render the degree, strength, modules or any other nod’s features. Similarly, the edge size and color can represent the weights for instance.On the other side, EEGNET is restricted to M/EEG signals while eConnectome offers the possibility of analyzing functional networks from ElectroCorticographic (ECoG) data.Many studies reported that scalp magneto/electro-encephalography (M/EEG) connectivity may bring relevant information for example about disrupted functional networks associated epilepsy  or with In this context, EEGNET provides the unique tool that combines the functional connectivity analysis from EEG data with the possibility of characterizing the networks using graph theory based analysis. This possibility of computing the network measures in EEGNET is in great interest for different application such as detecting disrupted nodes/edges properties during brain disorders.Further ways for software improvements may include the use of new visualization approaches or improve the existing ones. For instance, EEGNET will be updated to visualize modular partitions of brain networks, allowing for comparisons to well-studied brain networks . The circular view of the brain network used in different tools such as CVU will be also included in EEGNET. Analyzing the dynamics of the identified networks is an important direction of future work, by including algorithms for functional connectivity states for instance .The current version of EEGNET does not provide all preprocessing features. Different preprocessing modules will be included in the next version of EEGNET such as bad channel/trials and artifact removal . EEGENT In the current version of EEGNET, the analysis can be realized on a single subject or on averaged data. In the next version, group analysis will be included in order to study the inter-subject variability and the possible difference between subjects or/and conditions. Concerning the inverse problem algorithms, three different algorithms are integrated in the current version: the Minimum Norm Estimate (MNE), the weighted MNE and the Low resolution Brain Electromagnetic Tomography (LORETA). Descriptions about these methods can be found in . Other aWe have developed a new software tool called EEGNET. The main objective of this tool is to cover the complete processing framework from the M/EEG pre-processing to the identification of the functional brain networks. EEGNET includes mainly the calculation of the functional connectivity between scalp M/EEG signals as well between reconstructed brain sources obtained from the solution of the inverse problem. It also includes the characterization of the brain networks by computing the network measures proposed in the field of graph theory. EEGNET provides user-friendly interactive 2D /3D brain networks visualization.


Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced.Database URL:http://argo.nactem.ac.uk Caenorhabditis elegans proteins from the literature and brought about an 8-fold increase in curation efficiency (http://www.biocreative.org) have fostered the synergy between biocuration efforts and text-mining solutions. The user-interactive track of the latest workshop saw nine Web-based systems featuring rich graphical user interfaces designed to perform text-mining-assisted biocuration tasks. The tasks can be broadly categorized into the selection of documents for curation, the annotation of mentions of relevant biological entities in text and the annotation of interactions between biological entities  is a multi-user Web-based workbench for collaborative development and evaluation of text-processing workflows . Each of these three steps is highly configurable by the choice of elementary processing components that best fit the task at hand. For example, reading source data may be accomplished by deserializing user-uploaded files in various formats, such as plain text, XML and RDF, as well as by fetching data from remote Web services, such as search engines.Argo is available as a Web application and thus is accessed with a Web browser. Its graphical user interface (GUI) provides access to the complete functionality of the workbench.Users interact with Argo primarily by developing workflows and—if source data is not fetched remotely—by uploading their documents for processing with the workflows. Workflows are defined by users by visually arranging elementary components into interlinked graphs using a dedicated graphical block diagram editor shown in Workflows created by users are listed and managed in the Workflows panel  for ensuring interoperability of individual processing components by defining common data structures and interfaces. Annotations created in this architecture must adhere to customizable and well-defined annotation schemata or type systems. Each processing component imports (and supports) one or more such type systems and is responsible for populating a common annotation structure that is passed between components in a workflow for further processing.Argo supports and is based on the Unstructured Information Management Architecture . The arcThe actual processing of workflows is carried out on a multi-core server, with additional support for execution on high-throughput cloud computing frameworks, such as HTCondor.Manual Annotation Editor, a graphical interface that lets users view, modify or delete existing annotations, or add new ones [We use the term ‘annotation’ to denote any added information that is not part of the original data (document). They include span-of-text annotations as well as non-text-bound annotations that may contain primitive attributes  as well as references to other annotations]. The editor is the primary interface between biocurators and the system. The functions of the editor, a fragment of which is shown in Selecting a document for annotation from a list of available (previously processed) documents;removing or reassigning labels to already  annotated spans of text;adding new annotations by selecting a span of text and assigning a label to it from a set of available labels ;editing annotation features (attributes) via an expandable tree structure (shown on the right-hand-side panel in support for overlapping (intersecting) annotations;a GUI for assigning identifiers from external databases see ; andfiltering of annotations by semantic labels.Argo includes several features that render the workbench suitable for curation activities. In particular, one of the features allows users to manually intervene in the processing of a workflow by means of user-interactive processing components. An example of this type of component is the Relationships or interactions between biological entities are constructed by first marking spans of text in the central panel shown in The fundamental building blocks of workflows are processing components or analytics. Argo has an ever-growing library of components whose functionalities range from data reading/writing to NLP and text-mining analytics, to annotation manipulation and evaluation. Argo facilitates text-mining-assisted biocuration by means of task-specific highly customizable workflows. The workbench’s wide range of available components allows users to define semi-automatic curation workflows based on their specific needs.The workflow shown in www.nactem.ac.uk/Kleio), which indexes PubMed abstracts and facilitates faceted search  , plays aWe have undertaken this case study on metabolic process concept annotation with the following objectives: (i) to measure the accuracy of automatic annotation by evaluating the performance of the underlying text-mining components in Argo against human curators, and (ii) to quantify the feasibility of semi-automatic curation in Argo by collecting feedback from the curators and by comparing time spent on text-mining-assisted annotation against that on purely manual annotation. The outcome of this study was aimed at eventually facilitating the development of methods for the semi-automatic curation of biochemical reaction databases.http://ctdbase.org/help/ixnQueryHelp.jsp#actionType) of the Comparative Toxicogenomic Database (CTD) (The known activity of cytochrome P450 46A1 (P450 46A1) is 24(S)-hydroxylation of cholesterol. This reaction produces biologically active oxysterol, 24(S)-hydroxycholesterol, and is also the first step in enzymatic degradation of cholesterol in the brain.  [Source: PubMed abstract 14640697]In designing the task, we follow the definition of metabolic process from the chemical–gene interaction types ontology (se (CTD) , i.e. ‘t24(S)-hydroxylation of the compound cholesterol by the enzyme cytochrome P450 46A1, resulting to the production of 24(S)-hydroxycholesterol. Based on the concept types frequently involved in metabolic processes, we defined the curation task as the annotation of chemical compounds (CCs), genes or gene products (GGPs) and expressions signifying metabolic processes (triggers). Both CCs and GGPs may play the role of reactant , product (entity into which the reactant is changed) or modifier  in a metabolic process. The process itself is organized around a trigger word, often in the form of a verb, verb nominalization or adjective, e.g. generates, hydroxylation, acetylated.Described in these two sentences is a metabolic process, specifically, the The curators were asked to demarcate the metabolic process concepts and assign a corresponding semantic label, i.e. any of CC, GGP or trigger. As this task aims to ultimately facilitate the semi-automatic curation of biochemical reaction databases, it was desirable that the concepts be linked with entries in relevant biochemical resources. An additional requirement, therefore, was to assign to each concept a unique identifier from the following three vocabularies/ontologies: ChEBI  for CCs,Supplementary Materials for the reader’s convenience.To elucidate the specifications of the task and to foster agreement among annotators, we prepared a set of annotation guidelines describing the scope (i.e. what should be considered for marking up) and span (i.e. how to demarcate concepts within text) together with illustrative examples. The annotation guidelines, as well as the instructions, for using Argo for this particular task were made publicly available on Argo’s main website. They are also included as Manual annotation: This workflow reads PubMed abstracts and opens a Manual Annotation Editor for a curator to tag the entities of interest without any support from automatic processing available in Argo. It takes a list of PubMed abstract identifiers as input (by means of the PubMed Abstract Reader) and uses the XMI Writer to store the annotated documents in XMI format.Automatic annotation: Purely automated and not involving any manual intervention from the curators, this workflow reads PubMed abstracts and performs recognition and normalization of the entities of interest. The objective of this workflow is to automatically ‘pre-annotate’ the input abstracts for later manual inspection . After fetching abstracts by means of the PubMed Abstract Reader, the workflow proceeds to the segmentation of the documents with the GENIA Sentence Splitter. This is followed by tokenization, part-of-speech and chunk tagging with the GENIA Tagger, which additionally performs recognition of GGPs. Chemical compounds and trigger words are then recognized by the OscarMER component, which is capable of recognizing six different types of chemically relevant concepts, including chemical molecules and reactions. An instance of the SPARQL Annotation Editor was introduced into the workflow to facilitate the transcription of the semantic types from the preceding recognizers into a unifying, compact and simplified annotation schema for future use by the curators. Automatic normalization  is then facilitated by the three linker components , which use the Jaro-Winkler string similarity algorithm  and opens a Manual Annotation Editor for a curator to correct  the automatically recognized annotations. The annotated files are saved in XMI format by the XMI Writer.We randomly selected a subset of 60 PubMed abstracts from those that are tagged in the CTD as relevant to different types of metabolic processes. To draw parallels between purely manual and semi-automatic curation, we split the original data set into two equal parts and prepared a total of three workflows:Supplementary Materials).Although the second and third workflows could be combined into a single workflow, for the purposes of evaluation they were defined separately. We made the three workflows publicly available in Argo and accompanied them with detailed instructions available on the Argo website , semantic label and the corresponding unique identifier from the relevant external resource. The annotations have been made available in the XMI, BioC and RDF formats with the aid of the various serialization components available in Argo see .Together with the BioCreative track organizers, we recruited four curators with expertise in the curation of biochemical entities. One annotator was experienced in the semantic annotation of the PubChem databases . Two werWe analyse the curation task quantitatively by computing effectiveness and agreement metrics over the sets of annotations produced by the curators, and qualitatively by reporting on the received feedback from the curators and the organizers.One curator annotated mentions of all three concept types; the other two who specialize in ChEBI curation opted to focus only on chemical compound annotation. Consequently, we had at our disposal one set of abstracts with annotations for CCs, GGPs and triggers and two sets with only CC annotations.F-score for the different subsets and curators is given in F-scores are at the level of 60 and 81%, respectively. For CCs, the F-score is 56% for one of the curators and rises to 74–75% for the other two. This discrepancy can be explained by looking into the scores for individual subsets. Whereas the performance of the automatic workflow is comparable among the three curators on the ‘manual annotation’ subset, it deviates significantly on the ‘manual correction” subset with the F-score values as high as 95%. This indicates that the presence of annotations affected the behaviour of the curators who gladly accepted most of the automatic propositions.In evaluating Argo in terms of the performance of its automatic processing, we compared the annotations generated by the ‘Automatic annotation’ workflow on the 60 PubMed abstracts against the curators’ annotations drawn from the outputs of the ‘Manual annotation’ and ‘Manual correction’ workflows, i.e. the curators’ annotations were treated like gold standard. A detailed breakdown on precision, recall and F-score for the majority voting, albeit marginally, when compared with the individual scores.The pair-wise inter-annotator agreement for CCs ranged between 67 and 84% in F-score with the two annotators specializing in CC achieving the upper bound. We also analysed how well each of the curators as well as the automatic workflow reconstruct chemical–gene interactions that are stored in the CTD for each PubMed abstract. Each chemical–gene interaction in the CTD consists of the identifiers of the participants and the type of interaction. It must be noted, however, that the annotations produced by the human annotators and our automatic annotation workflow are not directly comparable with the CTD annotations for several reasons. While multiple instances of the same concept are contained in our corpus together with their in-text locations, the CTD stores only a per-document list of unique concepts without any location information. Additionally, we had to use approximate matching when mapping our GGPs and CCs to the identifiers of genes and chemicals in the CTD. In contrast, the interaction trigger words were mapped directly, as the task required the curators to link such words to entries in the CTD. Another source of incompatibility stems from the fact that for each abstract in the CTD, the database holds only identifiers of those chemicals and genes that directly participate in interactions mentioned in an abstract. Consequently, the list of curated CTD concepts might not contain all of the concepts mentioned in a document. In contrast, our task was designed to include the annotations of all of the mentions of chemicals, genes or gene products and metabolic process triggers. F-score, which is closely followed by the automatic workflow’s F-score at the level of 51%. The biggest differences can be observed among annotations of CCs where the aforementioned curator outperformed the other two as well as the automatic workflow by a 10%-point margin. However, the F-scores for the three curators are more balanced for the ‘manual annotation’ subset of abstracts. This shows yet again, that two of the curators could have been biased by the presence of automatic annotations.As expected, the best results were obtained for trigger words, as they directly corresponded to the entries in the CTD. The only curator who produced trigger word annotations scored at the level of 54% in The differences in the time spent on the task between the purely manual and the text-mining-assisted curations showed to be inconclusive. One of two curators who reported their times recorded a time reduction by a third with the text-mining-assisted set-up, whereas the other one reported an increase by a fifth. This inconsistency may be explained by the fact that the curators distributed their work over a month period, which may have affected their time keeping. We also note that the comparison baseline in our evaluation set-up was already demanding. Both the manual and assisted curation configurations featured the same manual annotation editor, which was virtually the only interface between the curators and Argo. It is a set-up that reaches far beyond the traditional curation process involving manually extracting and transferring relevant information from papers to spreadsheets. It is also worth noting that the time spent on automatic processing is only a fraction of the total time of the curation task. The initialization of resources, which is performed only once per the entire batch of documents, takes less than a minute, whereas the processing of a single document takes a few seconds.The curators were also asked to provide feedback on various aspects of the system in the form of a survey carried out by the track organizers. The overall scores for experience, system rating and recommendation were high for two of the curators who scored the system 12 and 14 out of 15 and low for the other two, both rewarding only 5 points. Such polarized scores may be the result of the amount of time the curators dedicated to learn the system. The curators were asked to go through a series of tutorials to familiarize themselves with Argo before proceeding to the annotation task, which may have been neglected, e.g. one of the curators admitted to proceeding to the task without taking any training. We also decided to leave the configuration of workflows to the curators themselves to validate whether performing this task is suitable for users with a presumably limited technical background. The versatility and—inevitably—complexity of the system posed a barrier to the full use of the workbench for two curators.To ease the use of Argo for curators, we have developed several new functionalities based on the received feedback. The biggest single inconvenience for the curators appeared to be the necessity of creating a workflow—albeit a simple one—to view or edit already saved manual annotations. We have tackled this problem by incorporating a document editing functionality directly in the Documents panel: users may now simply browse the documents in their space and open a selected one in the Manual Annotation Editor.We also observed that some annotations made by curators included leading or trailing white-space characters . To alleviate this issue, we introduced a feature in the Manual Annotation Editor that adjusts annotation spans to automatically exclude such characters. This, however, is made optional, as other annotation tasks may allow annotation boundaries to lie inside words. In fact, some of the annotations made by the curators span only a fragment of chemical expressions.Other improvements included more convenient access to annotation guidelines during manual annotation process and several other graphical usability changes.In this section, we present a short review of available Web-based biocuration platforms, conducted as a qualitative comparison of Argo against the other systems that participated in the interactive track of the BioCreative IV workshop.Some of the participating systems, the CellFinder curation pipeline , BioQRatMost of the participating systems were tailor-made for specific subject domains, therefore restricting their applicability to other biocuration tasks. The CellFinder curator and RLIMS-P systems are both tightly coupled with task-specific text-mining pipelines, requiring significant effort in adapting them to new curation projects. The named entity recognition and event extraction tools used by the CellFinder curator, for instance, were based on dictionaries and statistical models focussed on cells and genes. Similarly, RLIMS-P was developed as a rule-based system particularly for extracting phosphorylation events involving proteins acting as kinases, substrates and sites. Although both ODIN and BioQRator are also closely tied to specific tools [i.e. OntoGene  and PIE To a certain extent, some of the aforementioned biocuration systems render support for interoperability with external tools. Driven by text-mining components in the form of RESTful Web services, MarkerRIF and Egas can be integrated with other tools implemented in the same standard. BioQRator and Egas support the storage of annotations in the BioC format, allowing other BioC-compliant systems to read and process the resulting data. In comparison, Argo advances interoperability to a much greater extent by the availability of data serialization and deserialization components supporting exchange format standards such as XMI, RDF and BioC. Argo also features RESTful versions of these components, which makes it possible to deploy workflows as Web services.Primarily a text-mining workbench, Argo features a number of capabilities that render the system suitable for performing curation tasks. Given the nature of the system, Argo is best fitted for tasks related to the extraction of information from data (such as relevant concept recognition and concept interaction identification), but also exhibits strong potential in supporting document triage, as demonstrated by the use of a search engine reader component.The curation task carried out for the purpose of evaluating the workbench showed that the introduction of automatically generated annotations, which were meant to support the manual curation effort, tend to influence the decision of curators. Harmonized and impartial results could be obtained by the active collaboration of all curators involved in an annotation effort, as well as the automatic processing to speed up the task. Such active collaboration is already possible in Argo and is realized by means of the Manual Annotation Editor. The editor allows several users to remotely work on the same document (or a set of documents) and see each other’s changes in their respective instances of the editor in real time.Argo may be used by biocuration teams as a one-stop solution without any additional tool-development efforts; however, this type of use has its limitations. The current state of Argo will appeal to teams that are happy with working with their data through the functionality provided by the Documents panel and the currently available components facilitating document triage and curation result serialization. Further integration with certain teams’ existing infrastructure (particularly databases) can be achieved by the development of dedicated components that can be included in Argo workflows by the use of the Generic Listener component, which may be used to, for example, save results in a specific remote database. This type of integration, however, does require some development effort. To accelerate such efforts, a software development kit is provided together with sample code.Argo has a clear advantage over other systems in terms of the flexibility of defining biocuration tasks. The versatility of the system, however, comes with a complexity that impedes its usability when compared with other solutions. The evaluation of the workbench by biocurators participating in the BioCreative IV challenge revealed that two of the four curators struggled with the task when attempting it without prior training. In contrast, the other two curators who underwent the training rated the system highly.Drawing from the received feedback, we identified areas of improvement. In particular, we have already addressed the issue of directly editing annotations in users’ documents without the need to build a workflow, thus shifting the workflow-centric nature of Argo to a document-centric one. We would also like to explore its already available but underexploited resource sharing capabilities. Specifically, we are planning to draw a clear distinction between capabilities designed for technical users who are adept at building workflows, and users who have the domain expertize needed for validating the results of the processing of such workflows.


One of these mechanisms, the affinity maturation process, works in an evolutionary-like fashion: after binding to a foreign molecule, the antibody-producing B-cells exhibit a high-frequency mutation rate in the genome region that codes for the antibody active site. Eventually, cells that produce antibodies with higher affinity for their cognate antigen are selected and clonally expanded. Here, we propose a new statistical approach based on maximum entropy modeling in which a scoring function related to the binding affinity of antibodies against a specific antigen is inferred from a sample of sequences of the immune repertoire of an individual. We use our inference strategy to infer a statistical model on a data set obtained by sequencing a fairly large portion of the immune repertoire of an HIV-1 infected patient. The Pearson correlation coefficient between our scoring function and the IC germinal centers in lymph nodes and spleen prune out poorly binding receptors and clonally expand good binders. Thanks to high-throughput sequencing techniques it is now possible to have access to a fairly representative sample (of the order of 105 to 106 sequences) of the immune repertoire of a given individual. Our approach is to first exploit this large amount of sequence data to infer a statistical model for the sequenced portion of the immune repertoire, and then to use the inferred probability of this model as a score when predicting the neutralization power of a given antibody sequence for the antigen of interest. The results we obtained on a specific data set of sequences of an HIV-1 patient show that our score correlates very well with experimentally assessed neutralization power of specific antibodies of known sequence. The performance of the method crucially relies on the ability of our model to account for long-range intragenic epistatic interactions between residues along the whole antibody chain.Affinity maturation is a very complex biological process which enables activated B-cells to produce antibodies with increased affinity for a given antigen. Once B-cells begin to proliferate, each of the progeny cells introduces mutations in the antigen binding region in order to explore different affinities for the antigen. Selection rounds occurring in the so-called  The prediction of antibody  affinity for antigens is among the most interesting open challenges across bioinformatics and structural immunology. Most of the current methods rely on the structures  of both antibodies and their cognate antigens to predict their binding affinity. Currently, available methods are time demanding and, more importantly, their predictions are hard to assess , 3. On te.g. Deep Sequencing, and Next Generation Sequencing), Repertoire Sequencing (Rep-Seq) experiments  modeling of repertoires, as it has been proven in the case of zebra-fish Abs  and humaA number of statistical mechanics inspired methodologies have been recently successfully devised to analyze evolutionarily related proteins for inferring structural properties and, in particular, residue-residue contacts . In partHere we exploit the evolutionary nature of the affinity maturation process by applying a MaxEnt inference techniques originally developed for the analysis of homologous protein families. The above mentioned plethora of model inference methods aim at reconstructing a reliable contact map from the space of homologous protein sequences through an analysis of residues coevolution that disentangle indirect correlations, but in our context, they provide little information on Abs internal structure. However, the inference procedure provides a natural and reliable scoring function (see Section “Inference Methods”) from the space sequences to that of binding affinity for the target antibody related to the probability for a sequence to appear in the data set that we can use as a proxy to the binding affinity to the antigen, in the spirit of series of recent publications –23 wherein silico the effect of mutations related to disease and antibiotic drug resistance.Finally, we report that very recently maximum entropy modeling has been also used in  to prediIn the present work, we apply MaxEnt methods to study the affinity maturation process on publicly available data from an HIV-1 infected donor 40]). TheS1 TextMethods: Preliminary deep sequencing data analysis, multiple sequence alignment, clustering analysis, Supplementary results: Affinity predictions, structural predictions, internal contacts, ab-antigen interactions.(PDF)Click here for additional data file.


In this article, embedded optical fiber Bragg grating (FBG) sensors are used to analyze the vibration characteristics of two real-life composite components. The first component is a carbon fiber-reinforced polymer automotive control arm; the second is a glass fiber-reinforced polymer aeronautic hinge arm. The modal parameters of both components were estimated by processing the FBG signals with two interrogation techniques: the maximum detection and fast phase correlation algorithms were employed for the demodulation of the FBG signals; the Peak-Picking and PolyMax techniques were instead used for the parameter estimation. To validate the FBG outcomes, reference measurements were performed by means of a laser Doppler vibrometer. The analysis of the results showed that the FBG sensing capabilities were enhanced when the recently-introduced fast phase correlation algorithm was combined with the state-of-the-art PolyMax estimator curve fitting method. In this case, the FBGs provided the most accurate results, i.e., it was possible to fully characterize the vibration behavior of both composite components. When using more traditional interrogation algorithms (maximum detection) and modal parameter estimation techniques (Peak-Picking), some of the modes were not successfully identified.The measurement of the internal deformations occurring in real-life composite components is a very challenging task, especially for those components that are rather difficult to access. Optical fiber sensors can overcome such a problem, since they can be embedded in the composite materials and serve as  Moreover, they are resistant to corrosion and have excellent fatigue life [etc.); (iv) they are characterized by a long lifetime (more than 20 years), and they are stable over time ; and (v) they can be used for production monitoring [et al. [Fiber-reinforced polymers (FRPs) are a class of composite materials that are obtained by assembling together a reinforcing phase and a matrix phase ,2. The rgue life ,2,3. Thigue life . In the gue life . Howevergue life  since:  algorithm, while the second is the novel fast phase correlation (FPC) [In this paper, we describe the capability of full-spectrum measurements of embedded FBG sensors to perform modal analysis of two real-life industrial composite components. The first component is a CFRP automotive control arm, which is part of an automotive rear wheel suspension system. The second component is a GFRP hinge arm designed for the wing leading-edge high-lift device of a modern aircraft. In the original design, such a component was meant to be made of CFRP. However, to provide a proof of concept and to contain the cost at the same time, this research was conducted on a preliminary prototype made of GFRP. Both composite components were manufactured via the resin transfer molding (RTM) technique . During on (FPC) ,44 algoron (FPC)  and the on (FPC) ,47. For on (FPC) . The anaA fiber Bragg grating (FBG) is an optical fiber where a grating is inscribed inside the fiber core . Within L and Λ indicates the grating pitch , the FPC offers several benefits. Firstly, it produces precise and accurate results even when the wavelength resolution is poor and/or the reflected peak is noisy or partially distorted. Secondly, the FPC has a very high execution speed; therefore, it can be used for dynamic sensing applications where continuous monitoring is required. Finally, when employed for obtaining strain frequency response functions, the FPC allows one to achieve higher SNR levels than other methods. More information on the performance of the FPC algorithm is contained in Lamberti et al. ,44,55.m number of modes:A fundamental aspect related to the employment of FBG sensors to dynamic tests is represented by the application of modal analysis techniques capable of relating the measured FBG responses to the dynamic features of the structure under investigation. Since FBGs measure strains, a strain modal formulation has to be adopted ,57. Accor-th modal displacement vector, r-th generalized modal coordinate, t is the time and T indicates the transpose. A similar relation exists between the modal displacement vector In Equation , {u(t)} Using Equations  and 8),,8), Equaω is the excitation angular frequency, r-th modal mass, modal stiffness and modal damping ratio, and Moreover, the relationship between (t)} is:qr(t)= and by retaining only the system parameters  that are stable  between one estimation and another. Mathematical poles induced by noise in the measurements appear to jump around in the stabilization diagram without being stable for all of the model orders. The PolyMax method used in this article has the interesting property that the non-physical poles are estimated with a negative damping ratio, so that they can be automatically excluded. An exhaustive explanation of the PolyMax estimator is outside the scope of this paper. Additional information can be found in [Equation  can be ethe SFRF :13)Sϵϵ(Sϵϵ((12)H-Picking  and Poly-Picking ,47 modalfound in ,47.The CFRP component used in this work is an automotive control arm part of a rear wheel suspension system .The design of this CFRP control arm was carried out within the framework of the European research project Cornet DeMaCo . The comThe production of the CFRP component started with the preparation of the foam core by CNCmilling. Successively, the two metallic insertions were put in place using two unidirectional (UD) layers of dry carbon fibers b runningrmed CFR c was insrmed CFR d where ai.e., after post-curing). The spectra were acquired with an FBGS scan FBG804D interrogator [i.e., induced birefringence) of FBGi.e., pressure) setting in because of the resin curing. The FBGs embedded between two UD layers reveal a lower shrinkage thanks to the supporting stiffness provided by the carbon fibers. rrogator , which hThe second component analyzed in this research is a GFRP hinge arm, which is part of a wing-slat system. Such a component was designed in collaboration with the company ASCO  and manuIt must be noted that in the original design, this hinge arm is made of carbon fiber reinforcements. However, to provide a proof of concept and to contain the cost at the same time, this research was conducted on a preliminary prototype made of GFRP. The CFRP control arm and the GFRP hinge arm were tested to retrieve their modal parameters . The modal parameter estimation was performed using the strain measured by the embedded FBGs together with the out of plane velocities acquired via a laser Doppler vibrometer (LDV). In particular, the Polytec PSV-400 laser Doppler vibrometer was used. The experimental setup and the procedure adopted to test both components are displayed in ® reflective spray, which consists of a suspension of an inert white powder in a quick-drying solvent. An electromechanical shaker was mounted on the support frame and attached to the component under test (first to the CFRP and successively to the GFRP arm). A Schroeder multisine [®, amplified and sent to the shaker through an NI USB-6341 [ω; therefore, Equation , rather than experimental modal analysis . In effect, the main issue of performing experimental modal analysis consists of achieving a perfect synchronization of FBG, LDV and force transducer measurements. The authors are updating the MATLAB script used in this research in order to allow the acquisition of synchronized data for future works. Nevertheless, the output-only measurements performed in this article are a valid demonstration of the feasibility of using FBG sensors for vibration measurements in operating conditions. The procedure described above was used first for the CFRP component and successively for the GFRP aeronautic hinge arm.The CFRP automotive control arm was tested using the experimental procedure described in For OFpeak see , resultirted see . The meaThe second natural frequency and damping ratio cannot be identified using the Peak-Picking technique, since the FBG power spectra show only one clearly visible resonance. However, the second resonance can be identified when the PolyMax estimator processes the FBG data demodulated with the FPC algorithm. Therefore, the combination FPC-PolyMax guarantees the best results. The fact that the FBG spectra of The aeronautic hinge arm was tested using the same experimental procedure adopted for the automotive component and outlined in Both power spectra are very similar to the LDV magnitude of -The FPC demodulation performed better than the MD, especially in the case of a distorted reflected Bragg peak. Particularly, the FPC provided a signals with better signal-to-noise ratios than those obtained via the MD, making, therefore, the identification of the modal parameter more accurate.-The PolyMax estimator processed the FBG demodulated signals and identified the component modal parameters better than the Peak-Picking estimator. Peak-Picking was not able to retrieve either the second resonance of the automotive control arm or the third resonance of the aeronautic hinge arm. This resonance could be identified with the PolyMax technique.-The combination FPC-PolyMax guaranteed the most accurate results, being able to treat both distorted and undistorted FBG peaks and to identify modal parameters associated even with barely visible structural resonance. The MD-PolyMax resulted in being less accurate and even failed in one instance.-The estimation of the component modal frequencies was in general one order of magnitude more accurate than the identification of the damping ratios. This is a well-established phenomenon in modal analysis.In this work, the capability of embedded fiber Bragg grating (FBG) sensors to perform the modal analysis of real-life industrial composite components was investigated. The first component under test was a CFRP control arm used for an automotive rear wheel suspension system. The second component considered for the analysis was a GFRP hinge arm, designed to be incorporated inside a wing leading-edge high lift device. Both components were manufactured via resin transfer molding (RTM) and instrumented with embedded multiplexed FBG sensors. The modal analysis was performed by exciting the two components with a shaker and by processing the FBG dynamical measurements with state-of-the-art algorithms. For the demodulation of the FBG signals, two different algorithms were employed: the first is a maximum detection (MD) algorithm, which is conventionally implemented in several FBGs interrogators; the second is the fast phase correlation (FPC) algorithm recently developed by the authors. For the modal analysis, two estimation techniques were used: Peak-Picking and PolyMax. The analysis of the results showed that:in situ sensors for damage detection and monitoring purposes. The view is to start an experimental campaign focused on the fatigue testing of the two composite components and on the use of the embedded FBGs to detect and monitor incipient and propagating damage (such as delaminations).All of these results confirm that fiber Bragg grating sensors can be efficiently used for the dynamic characterization of complex real-life structures. Moreover, with the appropriate selection of processing algorithms, they can be valid substitutes of LDV measurements, especially for those cases where the accessibility to the test structures is an issue. In particular, the selection of the FBG demodulation algorithm is a crucial aspect. A good selection has to take into account the particular type of application the user is faced with. For instance, in the cases of surface-mounted FBG sensors interrogated with a high resolution device and expected to undergo high strain levels, the MD algorithm is still a good choice, since it will produce results similar to those achievable with more advanced algorithms like the FPC. On the other hand, for applications where spectral distortions are expected (FBG embedded in composites or exposed to harsh environments) or where the available interrogation device is limited in wavelength resolution, advanced algorithms like the FPC can really make the difference compared to a conventional MD peak tracking method. Future works related to this research will be focused on the exploitation of the embedded FBGs as


In orbital reconstruction, the optimal location of a predefined implant can be planned preoperatively. Surgical results can be assessed intraoperatively or postoperatively. A novel method for quantifying orbital implant position is introduced. The method measures predictability of implant placement: transformation parameters between planned and resulting implant position are quantified.t0), after creation of the defect (t1) and postoperatively after reconstruction of the defect using a preformed implant (t2). Prior to reconstruction, the optimal implant position was planned on the t0 and t1 scans. Postoperatively, the planned and realized implant position were compared. The t0 and t2 scans were fused using iPlan software and the resulting implant was segmented in the fused t2 scan. An implant reference frame was created ; the planned implant was transformed to the reference position using an Iterative Closest Point approach. The segmentation of the resulting implant was also registered on the reference position, yielding rotational  as well as translational parameters of implant position.The method was tested on 3 human specimen heads. Computed Tomography scans were acquired at baseline with intact orbits (Measurement with the Orbital Implant Positioning Frame proved feasible on all three specimen. The positional outcome provided more thorough and accurate insight in resulting implant position than could be gathered from distance measurements alone. Observer-related errors were abolished from the process, since the method is largely automatic.A novel method of quantifying surgical outcome in orbital reconstructive surgery was presented. The presented Orbital Implant Positioning Frame assessed all parameters involved in implant displacement. The method proved to be viable on three human specimen heads. Clinically, the method could provide direct feedback intraoperatively and could improve postoperative evaluation of orbital reconstructive surgery. The orbit is one of the most complex regions of the face in terms of reconstruction. Inaccurate restoration of orbital anatomy can result in functional dissimilarities and a poor aesthetical outcome. Unfortunately, sequellae of complex reconstructions are not rare ,2. Recen®, Feldkirchen, Germany), the ideal location of the predefined implant, either preformed or patient-specific, can be assessed preoperatively. In navigation assisted surgery, the preoperative planning can be translated to the intraoperative setting and can provide the surgeon a guide map to the predetermined ideal implant location. The use of preoperative planning in combination with intraoperative navigation to aid the surgeon in reaching optimal surgical outcome is encapsulated in the surgical concept of CAS.The use of the Computed Tomography scans for orbital reconstructive surgery has extended beyond diagnostic imaging alone . By usinIn orbital reconstructive surgery, CAS provides the surgeon a target location of the preformed implant to provide the best possible anatomical reconstruction of the affected orbit. Through imaging of the acquired position either intraoperatively or postoperatively, objective assessment of the surgical result in comparison to the target result is possible. In literature, various methods have been described to quantify implant location in patients, ranging from linear measurements on individual CT slices to color-coded distance maps –15; the Since a target location is already provided in CAS, the actual implant location should be related to the planned implant location. The shape of the preformed orbital implant is known; if a reference frame is created, rotational and translational deviations can be quantified, providing a ‘true’ three-dimensional evaluation of implant position. Measures of rotation and translation will provide direct feedback to the surgeon on the necessary adjustments to obtain the ideal implant location. Patient dependency is eradicated from the equation, making comparison between patients and populations reconstructed with the same preformed implant possible. This study presents a largely automated position measurement method by means of a reference frame for the newly designed KLS preformed Orbital Implant ; this will hereafter be referred to as the Orbital Implant Positioning Frame (OIPF).The Orbital Implant Positioning Frame is visualized in t0), after creation of the orbital defects (t1) and postoperatively after implant placement (t2). The standard CT head trauma protocol was used . The proposed method of measuring implant position described below was tested on the human specimen heads, in order to test the position measurement and demonstrate its feasibility.Computer assisted orbital reconstructive surgery with a preformed titanium mesh plate was performed by LD on 3 human specimen heads with intact orbits, fixed in 2.4% formaldehyde. The specimen heads were supplied by the department of Anatomy of the Academic Medical Centre (AMC). Through a standard transconjunctival approach, complex orbital defects (Class III-IV) were created following the Jaquiéry classification ,19, witht0 and t1 scans were fused using iPlan software; a 3D stereolithographic model (stl) of the preformed orbital implant was imported in the dataset. The stl of the preformed implant was positioned at the ideal location for anatomical reconstruction, based on information about the unaffected orbital contour (t0 scan). Information on existing bony ledges of the affected orbit (t1 scan), which is needed for adequate positioning of the implant, was also taken into account in finding the optimal fit. Consensus on the optimal implant position was reached between observers RS and LD for all planned implants.The t2) scan was acquired, the t0 and t2 scans were registered with the use of the Image Fusion modality in iPlan. A threshold-based segmentation with threshold ≥1200 HU was generated to segment the implant in the registered t2 scan; afterwards, post-processing of the segmentation result was performed by RS to remove superfluously segmented bony tissue. Care was taken not to include the three screw rings extending proximally from the implant, since these could have been adjusted during the surgical procedure; these bended areas of the implant could hamper surface registration between planned implant and the surgically positioned one. The fixation screw was also removed from the segmentation result.After the postoperative (M0) from the iPlan software; the segmentation result was also exported in stl format (M2). The local coordinate frame of the planned implant was transformed to the global reference frame by matching the planned implant on the reference implant, with a best-fit matching approach  [The planned implant was exported as an stl model (gorithm) :M0,G=TLL→G) was applied to the segmented implant, thereby maintaining the relative orientation between M0 and M2, but transforming them both to the global coordinate frame:The same transformation  implant on the resulting  implant, in order to determine the transformation matrix between the planned and acquired implant position:The Iterative Closest Point (ICP) algorithm was also utilized to register the planned , yaw  and roll  were calculated using the following formula:From the resulting transformation matrix T0→2. Since transformation of the complete implant was described by the transformation matrix, the translation of other key points on the implant  could be calculated if desirable. These points are also indicated in xz-plane, in order to be able to compare results for left-sided and right-sided orbital reconstructions.The order of rotations in the resulting calculation was, by definition of the formula above, regarded as roll first, pitch second, and yaw third . The translational parameters of the origin in all three directions were also acquired from the transformation matrix Ta,b) and sagittal view (c) of the baseline (t0) scan. In both coronal views shown here, as well as the sagittal view, comparison to the implant floor would suggest an adequate position of the resulting implant. In x 2.5mm, translationy -2.7mm, translationz -0.5mm. A yaw combined with a lateral translation is present, and the target set by the planned implant position is obviously not reached. These translations and rotations will affect implant position at the inferior orbital fissure, as seen in In specimen 2. In the coronal slices, the orbital contour is nicely followed medially; laterally, there is a significant deviation between implant position and orbital floor. From the sagittal view it could be concluded that the implant is placed too far dorsal. In the coronal views, the cross-section through the resulting implant would be visualized at another level than the cross-section through the planned implant. This might explain the observed overlap between the planned and the resulting implant position medially, but with a lateral deviation. The three dimensional view, generated after segmentation, shows that a dorsal translation of the resulting implant is not the only translational error compared to the planned position; a lateral displacement is also present. Analysis with the Orbital Implant Positioning Frame shows that there are also a roll rotation in the resulting implant ; although this could not be concluded from the individual slices, it is clearly shown in the 3D view in specimen 3 is in close relation to the planned implant position. On all slices visualized in x -0.3mm, translationy 0.2mm, translationz -0.4mm. In comparison to the parameters found in specimen 1 and specimen 2, the dislocation parameters to the planned position are very small.The implant positioned in The goal of orbital implant placement is to reconstruct the bony orbit and to restore the orbital volume. Although Andrades et al. showed no direct correlation between implant position, orbital volume and clinical outcome , in moreThe planned position assuring optimal fit of the implant serves as the target in positioning of the implant during surgery. Image fusion of preoperative and postoperative scans provides the possibility to compare surgical result to the planning, to see if the target has been reached. A novel method for standardized position analysis of the acquired implant position is described in this article. This standardized position analysis compares the acquired position to the target position and is able to provide feedback to the surgeon about all implant dislocation parameters. A specimen study has been conducted to demonstrate the feasibility of the method to measure implant position; it would be of clinical interest to relate the dislocation parameters to clinical outcome such as resulting orbital volume, globe position and functional or esthetic deficits in an in-vivo study. From this clinical study it would be possible to establish an evidence-based range in which the difference between planned and acquired position is acceptable.In complex defects (Class III-VI) contour becomes a more important factor for repositioning the globe in a proper position ,18. PatiIn this study, a reference frame for a preformed implant has been designed. Rotation and translation between planned position and the segmented implant in the matched postoperative scan are calculated. In literature, various methods exist to assess implant position after surgery. Linear measurements between mirrored (unaffected) orbit and the positioned implant, on either one slice of the postoperative CT scan ,5 or mulIf (color-coded) distance maps are used for quantification of the acquired implant position ,12,13, eStoor et al. did choose the planned implant as the reference, and calculated the volumetric overlap between the planned implant and a segmentation of the acquired implant position . This voA disadvantage of the proposed method may be the influence of bending of the implant during surgery. The ICP algorithm searches the best possible fit between the surface of the planned implant model and the segmentation of the implant in the postoperative scan. The accuracy of the match on an intraoperatively bended implant will be hampered by its change in shape in relation to the planned implant. The concept of preformed implants ideally should make bending of the implant obsolete. Using only part of the surface of the implant for matching is possible, to exclude the bended areas and provide the best possible match on the region of interest of the implant. Another drawback of the method could be the choice of the reference axes. A different orientation of the implant’s reference frame will result in differences in the results. Care has been taken to design a logical and intuitive coordinate frame for the implant. It is strongly suggested that the same Orbital Implant Positioning Frame is used for a preformed implant, to be able to compare surgical outcome between different surgeons, methods and studies. Embedding an indication of one or multiple axes of the reference frame in the implant’s design, as well as making the reference implant used in this study available, could aid in standardizing the used reference frame and increasing comparability of studies.Postoperatively, the analysis method aids in accurate evaluation of surgical result and may assist in choices for re-operations in patients with suboptimal clinical outcome. If intraoperative imaging is performed, the proposed Orbital Implant Positioning Frame can quantify all parameters involved in the obtained implant position. Combined with 3D visualization of the implant dislocation, the Orbital Implant Positioning Frame can provide accurate and intuitive feedback to the surgeon about the necessary adjustments to reposition the implant on the planned position within the same surgical session. This produces a shift in the use of the Positioning Frame: it is not only used to measure position, but it can be used in the positioning process during surgery. The shift to a positioning frame actively providing feedback to the surgeon intraoperatively has even greater implications in combination with intraoperative navigation. The use of intraoperative navigation hardware makes tracking of the implant position within the OR possible. To guide the implant from the current position in the OR to the target position, it is necessary to relate current position to the target position. The Orbital Implant Positioning Frame fulfills this requirement and could thus be used to provide real-time feedback to the surgeon for positioning the implant at the target location.This study proposes a novel method of quantifying the outcome of orbital reconstructive surgery. All parameters influencing implant position are quantified, and at the same time the observer-related and patient-related factors are eradicated from the results. Clinically, this method is able to compare different surgical methods for predictability of the reconstruction, provide direct feedback intraoperatively and provide more thorough information about implant position to the surgeon postoperatively. It is also holds a valuable scientific possibility to compare the results in case-series and multi-center observations.S1 TableThe rotational parameters (in degrees) and translational parameters (in mm) are given for every specimen.(XLSX)Click here for additional data file.


Here we experimentally demonstrated the electromagnetically induced transparency (EIT) and Autler-Townes splitting (ATS) effects in mechanical coupled pendulums. The analogue of EIT and ATS has been studied in mechanical systems and the intrinsic physics between these two phenomena are also been discussed. Exploiting the Akaike Information Criterion, we discern the ATS effect from EIT effect in our experimental results. Based on the research of a simple pendulum, the coupled pendulums are widely studied during the past decades171820The classical analogues of such quantum phenomena also attract much attentions. However, classical analogues of EIT and ATS effects using mechanical systems are not reported by the researchers. Motivated by the all-optical analogues of EIT and ATS using classical coupled systems, we demonstrate the experiment using coupled mechanical systems to discern the ATS effect from EIT effect. Here in this study, the EIT and ATS effects analogues are experimentally observed in mechanically coupled torsion pendulums. And by using the Akaike Information Criterion (AIC), we theoretically discerned ATS effect from EIT effect.Here the experiment setup is shown in The amplitude-frequency characteristics curves (AFC curves) of the pendulum 1 are measured as shown in To study the coupled system further, we measured the AFC curves of coupled pendulums with the largest coupling strength of spring 4. As shown in To study the effect of different coupling strength, we use different coupling springs  of the coupled pendulums is measured under different coupling strength, and the AFC curves are shown as the blue lines in Here, ATS). Here Exploiting the Akaike’s information criterionIn conclusion, we have experimentally studied the analogue of EIT effect and ATS effect in coupled mechanical systems and provided a classical analogue of EIT and ATS in this work. We use AIC to test the transmission spectrum of the mechanical system and discerning the ATS effect from EIT effect. By changing the coupling strength, the system could approach the transition point from the ATS domain to the EIT domain, but hardly into the EIT domain. Furthermore, we theoretically analyzed the intrinsic physics of such results in the mechanical system. In addition, our work can be extended to study the parity-time-symmetric system23Here we introduce the theoretical mode of two mechanical oscillators as a three-level atomic system to describe the EIT and ATS effects. The coupled mechanical system could be described using the equations asK is the the stiffness of the coupling spring; Here To further simplify the equations, we assume that the frequency By substituting the steady-state solution to Similarly, by substituting the steady-state solution to Then, the displacement of the two oscillators could be written as the superposition of the two solutions asAnd the amplitudes of the two oscillators For one oscillator case in this coupled system, we set the pendulum 2 is fixed as How to cite this article: Liu, J. et al. Experimental distinction of Autler-Townes splitting from electromagnetically induced transparency using coupled mechanical oscillators system. Sci. Rep.6, 19040; doi: 10.1038/srep19040 (2016).