The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated.Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the Yeast, C.Elegans and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient.We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the application of those measures to efficiently guide the modularity detection process. We have empirically evaluated our approach in the specific context of real-world large scale biological networks, and have demonstrated significant savings in computational time while maintaining comparable quality of detected modules. The problem of modularity detection in networks has received considerable attention in recent literature -5. SpeciThere are three primary approaches to modularity detection: (i) top down (or divisive) techniques, in which a series of network partitions hierarchically decompose a network into modules, (ii) bottom up (or agglomerative) techniques, in which modules are constructed by adding elements to an initial seed, and (iii) force directed methods, in which suitably designed parameters drive nodes belonging to the same module to spatially proximate regions of space. There have also been investigations focused on relating various classes of methods .edge-betweenness, a metric that has received considerable recent research interest in the domain of modularity detection. Edge-betweenness is typically computed as the number of (pair-wise) shortest paths that traverse an edge in a network. This notion, which was first introduced by Anthonisse [One such divisive technique of interest is the Newman and Girvan (NG) algorithm , which uthonisse , can be modularity (Q), which is defined as follows:The output from the NG algorithm is a complete dendrogram, which decomposes a given graph down to individual nodes. Modules are extracted from this dendrogram by applying a quality metric called e is a k × k symmetric matrix whose element ij eis the fraction of all edges in the network that link vertices in module i to vertices in module j;k is the number of modules in the network;where, Tr(e) = ∑i eii, is the trace of e, which represents the fraction of edges in the network that connect vertices in the same module;i a= ∑j eij, are the row (or column) sums, which represent the fraction of edges that connect to vertices in module i;E|| denotes the sum of the elements of matrix E.||ij e= iaja.We observe that, in a network in which edges fall between vertices without regard for the modules they belong to, Q value measures the fraction of the edges that connect vertices within the same module minus the expected value of the same quantity in the network. If the number of intra-modular edges is no better than random, we get Q = 0. Values approaching Q = 1, which is the maximum, indicate strong modular structure [Q values for such networks with strong modular structure typically fall in the range from about 0.3 to 0.7. The modular decomposition of the network (from the dendrogram) with maximum Q value is considered to be the best split by the NG algorithm.The While the computation of modules using the NG algorithm has been shown to perform well in terms of quality of modules, its computational cost can be significant . This cost, in part, stems from repeated edge betweenness computations. Furthermore, a level of refinement in the output dendrogram to the individual nodes, is typically unnecessary from an application standpoint, often un-informative, and computationally expensive. Finally, the dendrogram requires additional post-processing to identify suitable modules based on quality measures associated with the modules. Computing the quality of each module corresponding to every node in the dendrogram is itself expensive. A stopping criterion that identifies a near-optimal point at which the process of iterative edge-removal may be terminated would significantly reduce the time and space complexity of the NG algorithm.p values of clusters as termination criteria [p value of a module is lower than a user-specified threshold. This does not guarantee that the optimal p value modules are found. Similarly, for data-sets for which precise models are not available, estimation of number of clusters is difficult. Neither class of techniques is directly applicable for divisive partitioning based on the NG algorithm.The problem of terminating divisive clustering is an important one, especially when the clustering method is itself expensive. A number of other approaches have been proposed--including use of criteria . Howevercriteria  stops thtarget edge-betweenness), based on initial values of edge-betweenness computed over the input network. In particular, we define the target edge-betweenness to be the geometric mean of edge-betweenness values of all edges in the input network . A detailed description of our algorithm is included in the Methods section.In this paper, we experimentally derive an optimized termination criterion for the NG algorithm  for a network of n vertices and e edges . This computation becomes prohibitively expensive in the context of large biological networks.1. The iterative removal of edges  is performed until all the edges are removed, leading to a time complexity of Q is calculated for every partition of a network in the dendrogram. This is necessary for determining optimal splits.2. The modularity Q value computations, we eliminate the second overhead altogether.The Gmean algorithm directly addresses these overheads in two fundamental ways: it terminates the process before all edges are removed, thus significantly reducing the first overhead. Since the termination criterion is computed just once , and does not rely on repeated Q values comparable to the maximum Q value from the NG algorithm--thus maintaining the quality of the identified modules, while significantly reducing runtime. We also use the Jaccard Similarity Coefficient (a measure of similarity between two sample sets) to show that the resulting modules from both the approaches are similar.Furthermore, we demonstrate that our algorithm results in modules with We validate our approach on the networks summarized in Table We perform our experimental evaluation using a parallelized approach  to impleNG RTand Gmean RTdenote the execution times for the NG and Gmean algorithms respectively. We define the percentage gain in computational time (τ) between the NG and Gmean algorithms, as follows:For a specific network under consideration, let We observe significant and consistent savings in computational cost with our proposed optimization . Figure In Figures quality of the modules produced using the modularity value Q. Figure In addition to quantitatively comparing and demonstrating that the modules resulting from our algorithm are comparable in number and distribution to the modules resulting from the NG algorithm, we also present a qualitative validation that the results are indeed statistically similar in terms of Jaccard Similarity Coefficient or the Jaccard Index is a statistic used for comparing the similarity and diversity of sample sets. The Jaccard Index measures similarity between two sample sets (say A and B), and is defined as the size of the intersection divided by the size of the union of the sample sets:The Jaccard Index is 1 if the two sample sets are exactly identical, and is equal to 0, if they have no overlap at all.J  for all pairs of modules A and B .We use this metric to show the similarity of the modules produced as the output by the NG and the Gmean algorithms. Specifically, we consider the modules produced by the algorithms as sample sets constituted by vertices and calculate the Jaccard Indices We define the percentage similarity score (λ) as the following:J  is the Jaccard Index for the modules A and B, one from the output of each algorithm;where J * is the ideal Jaccard Index for the modules A and B, one from the output of each algorithm * = 1, corresponding to perfect match, when the two modules A and B are exactly identical);Σ is the summation over all pairs of modules, one from the output of each algorithm.Table In this paper, we have proposed a novel termination criterion for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure, and the application of those measures to efficiently guide the modularity detection process. We have empirically evaluated our approach against existing techniques for modularity detection in the context of biological networks, and have demonstrated significant savings in computational time while maintaining comparable quality of detected modules.Q.In the NG algorithm, the edge-betweenness is computed for each edge in the network under consideration. The edge with the maximum edge-betweenness is identified and eliminated, followed by a recalculation of the edge-betweenness values of all the remaining edges in the resultant network. This process is iteratively repeated till no edges are remaining, thus generating a complete dendrogram which is then traversed to identify the partition with best modularity value target edge betweenness T that is used to determine termination of the algorithm. In particular, we propose that the recalculation of edge-betweenness and removal of the edges be stopped when the edge to be removed has a betweenness value less than T. More intuitively, we propose that for an edge to be considered to be an inter-modular edge, it must have betweenness value of at least T.Figure T:Based on extensive experimentation, we propose the following definition of G (e) is the geometric mean (gmean) of edge-betweenness values of all edges in the input network. Validation on real networks shows that this choice serves as a robust and high-quality termination criterion. Specifically, as stated in the results section, this choice produces a set of modules comparable in quality and quantity to those produced by the NG algorithm. We show this for a number of biological networks of interest. All biological network data used for the experimental study are from publicly available data sources [where  sources ,10.C.Elegans: Caenorhabditis elegans; gmean: Geometric Mean.The authors declare that they have no competing interests.TN investigated the problem of modularity detection and associated literature, proposed the optimization to the existing Newman and Girvan algorithm, and empirically evaluated the approach. MG helped with refining the proposed optimization and perform quantitative comparison. SS and AG provided guidance relative to the theoretical and practical aspects of designing/evaluating the algorithm. All authors read and approved the final manuscript.


In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system. Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on  Metabolism is crucial to cell growth and proliferation. Deficiencies or alterations in metabolic functions are known to be involved in many human diseases. For example, the pathogenesis of diabetes results from malfunction in the regulation of metabolic pathways, leading to alterations in insulin signaling, oxidative metabolism, and lipid/fatty acid metabolism . Dysreguin silico the global effect of perturbations on the network to generate hypotheses and help understand the mechanisms underlying the genotype-phenotype relationship.As a highly-connected complex reaction network wherein the functionality and the connectivity is strongly associated , changesIn this review, we first describe the reconstructions of global human metabolic network, and then introduce the constraint based modeling approach to analyze metabolic networks. We further discuss systems biology applications of the metabolic networks to study human physiology and pathology. Finally we highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system.The global human metabolic network has been manually curated based on an extensive collection and evaluation of the genomic and bibliomic data. The first two installation of the network were released in 2007: The Edinburgh Human Metabolic Network  and the The two networks (Edinburgh Human Metabolic Network and the human Recon 1), developed independently by different research groups, consist of many different genes and reactions. The Edinburgh Human Metabolic Network contains more genes and metabolites, but was not compartmented in its initial release. Compartmentalization requires assignments of metabolic reactions into different cellular organelles  and accounts for the transportation and exchange of metabolites between organelles. Human Recon 1 is a compartmented network which could be used in reconstructing predictive models for systems biology studies, therefore, most of the recent applications have been based on Recon 1. An overview of the publications thus far that used Recon 1 is reviewed by Bordbar and Palsson . NotablyThe reconstruction of the global human metabolic network uses a bottom-up approach. Researchers begin by compiling reactions of cellular metabolism to build a network through the collection of gene annotations, enzymes and pathway information from genome  and pathway  databases. Researchers then refine the network by manually collating literature evidences, including journal articles, reviews and textbooks on metabolic functions, biomass composition, growth conditions and gene-reaction associations. The constructed draft network is converted to biochemical models to evaluate the basic functionality, and simulations are performed to check for consistency with the current knowledge. The whole process runs iteratively to incorporate as much information and minimize gaps and inconsistencies. The protocol for the reconstruction process is available in .The major difference in a metabolic network as compared with other biological network, e.g., Protein-Protein Interaction, Protein-DNA network, is that the metabolic network represents a biochemical system that is charge-balanced, mass-balanced and compartmentalized. This not only provides information about whether there is an interaction, but also how it happens and what it produces as a biochemical reaction, and thus can be directly converted into mathematical equations based on the biochemical reactions, for model predictions. A reconstructed human metabolic network can be represented by a system of stoichiometric reactions. This system of reactions can be modeled as ordinary differential equations, however the reaction rate constants and metabolite concentrations are typically difficult to obtain, thereby limiting their applicability to small well-studied networks. However, since the stoichiometry of metabolic reactions are not organism or context-dependent but is fixed by mass balance, one could apply Constraint Based Modeling  to simuMathematical representation of reaction network and Constraint Based Modeling:S (Stoichiometric Matrix), with m compounds (rows) and n reactions (columns). The stoichiometric coefficients are negative for the substrates of each reaction, and positive for the products.Reactions: v (n by 1 vector) on all reactionsFlows: X (m by 1 vector) of all compoundsConcentrations: Assuming pseudo-steady state, the time derivative is zero, therefore:v that satisfies this equation is in the null space of S.So the flux distribution n > m results in an under-determined system that does not have a unique solution. Adding constraints permit a “feasible” solution to the system of equations, for example, a “flux capacity” constraint determines the upper and lower bounds of the flux through a reaction. Imposing mass balance and capacity constraints will define the space of feasible steady-state flux distributions of the network. Geometrically the space looks like a “flux cone” in the null space of S. A visualization of the “flux cone” is shown in [Z, which is a linear function of fluxes. An objective function could beIn the human metabolic network, shown in   to showc is a column vector which assigns weights to each reaction, Tc is the transpose of the vector c, and V is the flux vector through all the reactions. Optimization of the objective function Z identifies a unique (or multiple) set of flux configurations within the flux cone. The constrained linear optimization problem can be solved by linear programming [where gramming ,20.The form of the objective functions, constraints and the optimization problems can vary depending on the biological applications, which are different variants of the Constraint Based Modeling. For example, the Flux Sensitivity Analysis (FSA) estimates the objective flux change in response to perturbations in some reactions of interest . The Flui.e., condition/cell-type/tissue/organ specific) human metabolic systems in order to generate biological hypotheses and study the physiology or pathology of cellular processes. Since the global human metabolic networks (the human Recon 1 and Edinburgh Human Metabolic Network) are generic metabolic networks that collate information from all types of human cells, the reconstruction of a context-dependent network is required prior to in silico analysis of the particular system under investigation. Once the context-dependent reconstruction is obtained, one can simulate the metabolic phenotypes under different perturbations to identify essential gene targets or pathways, or predict cellular responses to different treatments. A summary of the pipeline for the systems biology applications of human metabolic network is shown in A central goal in the application of systems biology on the human metabolic network is to reconstruct and simulate context-dependent  or “absent” (low expression level) for the condition being investigated, then selects the active reactions according to their corresponding gene/enzymes’ expression level. For example, the Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm, developed by Becker et al. , uses ge [et al. . The app [et al.  developein silico analysis can be used to generate hypotheses on the cell growth, ATP production, or the states of specific metabolic functions upon perturbation [Constraint based modeling and simulation based on a condition-specific human metabolic network can be used to predict the flux distribution in the network for that specific metabolic phenotype. Such urbation .et al. [Modeling and simulations of a metabolic model of human kidney reconstructed with the GIMME algorithm were used to evaluate the metabolic phenotypes associated with the side-effects of a drug treatment . The sidet al.  further in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In summary, the reconstruction and simulation of human metabolic networks enable a systematic study of the regulatory system and is applicable to a wide variety of cases. One could rely then on Current reconstructions of the global human metabolic network are incomplete due to limitations in our knowledge of the complexity of the human genome. Both the human Recon 1 and Edinburgh Human Metabolic Network have gaps and missing parts. For example, there are many reactions in the networks that do not have corresponding regulatory genes/enzymes annotated. For the reactions with known genes/enzymes in either network, there are only around one thousand genes that are common to both, with some collated in only one of the networks. It would be better to combine the two reconstructions to achieve a more comprehensive network. Nevertheless, the integration would be difficult since the standards are different between the two networks—Different compound names, different reaction names, and different IDs are used for the network components.The community needs to drive the effort for better reconstruction and integration of these network models. A community approach of “reconstruction jamborees” ,38 was set al. [i.e., the lower a gene is expressed, the lower the flux that could be conducted through its corresponding reactions. Although it could be more sensitive to changes (noises) in the gene expression by directly associating the gene expression level with flux bounds, the approach is less dependent on the discretization of the gene expression and the present/absent call of the reactions, which could provide a more realistic model. It has been applied to bacteria to predict metabolic modulators and responses to different drugs [Applications of the metabolic model require reconstruction of context-dependent networks, thus computational approaches to automate the generation of condition-specific models are of great interest. Current approaches rely on present and absent (high/low expression) of the genes to determine potentially active reactions, which could be problematic since a small change in the threshold in the expression analysis could result in very different lists of present and absent genes and affect the reconstruction. Most genes’ expressions are not naturally bimodal but have many levels. A discretization process separates such expression levels into ON and OFF states with some arbitrary thresholds , thereby the analysis based on discretization will be affected by the threshold that is chosen. A change in the threshold will change many of the genes’ ON/OFF categorization, which could affect the network reconstruction, since the reconstruction process is also bimodal with reactions being absent or present according to the ON and OFF state of the genes. For example, a gene expressed a little lower than the median may be classified as absent/OFF, then the reaction it is associated with may be removed. However a gene is not necessary in an entirely OFF state when expressed at a low level, and a reaction may not necessary be completely abandoned just because there are fewer enzymes than normal. Current approaches rely on the stoichiometric constraints to mitigate the possible inconsistencies. Future reconstruction approaches could impose more realistic models of the functional relationship between genes and reactions. For example, a novel approach developed by Colijn et al.  associatnt drugs .With the rapid development of metabolomics, metabolic flux data becomes more readily available and novel approaches are being developed to more efficiently monitor flux distribution in mammalian cells with Metabolic Flux Analysis, by employing isotope-labeling  techniques . Researcet al. [M. tuberculosis to study host-pathogen interactions. Simulation of this metabolic system under different infection states helped identify differentially active pathways and potential gene targets. Another pioneering work by Lewis et al. [in silico model was used to analyze the gene expression data to identify changes in the metabolic states as well as important genes in Alzheimer’s disease. In both studies, the incorporation of the metabolic networks was based on manual curation, since there are no computational approaches for reconstructing a multi-cellular metabolic system to date. In the future we should expect novel approaches and applications to higher level systems modeling that involve different cells and tissues.Context-dependent metabolic models of different cells could be incorporated to achieve higher level systems to study cell-cell interactions. A recent study by Bordbar et al.  incorpors et al.  reconstrThe gene-reaction associations in current reconstructions of the human metabolic networks account for enzymes, isozymes, transcript variants and protein complexes, but however are limited to metabolic-related genes and enzymes. There are other layers of regulation of gene expression and enzyme activity, including transcriptional regulation, post-transcriptional and post-translational processes. Many genes involved in transcriptional or post-transcriptional regulation of the metabolic genes or enzymes could also affect the metabolic states, which are excluded from current applications. Although it is difficult to have a comprehensive model due to limitations of our knowledge of biological regulation, we could incorporate more information into the metabolic network to provide a better model of the regulation of the human metabolic system. Given the increasing amount of transcriptome data, protein interaction data and other high-throughput data, they could be capitalized upon to enhance the regulatory model. Future studies should consider incorporating multiple layers of regulation to better understand the molecular mechanisms behind the regulation of human metabolism.


Children with Autistic Spectrum Disorders (ASD) are frequently hampered by motor impairment, with difficulties ranging from imitation of actions to recognition of motor intentions. Such a widespread inefficiency of the motor system is likely to interfere on the ontogeny of both motor planning and understanding of the goals of actions, thus delivering its ultimate effects on the emergence of social cognition.We investigate the organization of action representation in 15 high functioning ASD (mean age: 8.11) and in two control samples of typically developing (TD) children: the first one, from a primary school, was matched for chronological age (CA), the second one, from a kindergarten, comprised children of much younger age (CY). We used nine newly designed behavioural motor tasks, aiming at exploring three domains of motor cognition: 1) imitation of actions, 2) production of pantomimes, and 3) comprehension of pantomimes. The findings reveal that ASD children fare significantly worse than the two control samples in each of the inspected components of the motor representation of actions, be it the imitation of gestures, the self-planning of pantomimes, or the  comprehension of observed pantomimes. In the latter task, owing to its cognitive complexity, ASD children come close to the younger TD children’s level of performance; yet they fare significantly worse with respect to their age-mate controls. Overall, ASD children reveal a profound damage to the mechanisms that control both production and pre-cognitive “comprehension” of the motor representation of actions.Our findings suggest that many of the social cognitive impairments manifested by ASD individuals are likely rooted in their incapacity to assemble and directly grasp the intrinsic goal-related organization of motor behaviour. Such impairment of motor cognition might be partly due to an early damage of the Mirror Neuron Mechanism (MNM). Yet, the relevance of motor disorders in autistic children was downplayed for years, since the varieties of motor impairments are camouflaged by an overwhelming lack of social and emotional reciprocity. Furthermore, no theoretical frame was available in the early studies on motor impairment that could reliably intersect the failure of anticipatory postures, stereotyped behaviours, imitation deficits, clumsy gait, and other motor impairments with the impairments of sociability and communication Children’s aloneness from the beginning of life is a marking trait of autistic disturbances. Indeed, as Kanner pointed out, “the children’s inability to relate themselves in the ordinary way to people and situations from the beginning of life” These difficulties notwithstanding, it became increasingly clear that motor disorders were a core component of the Autistic disorder. More than 30 years ago, Marian De Meyer suggested that “body imitation failure” action intention as well  of the macaque monkey, of a class of motor neurons, mirror neurons that discharge both during the execution and the observation of goal-directed motor acts The relevance of the mirror mechanisms was further enhanced by their phylogenetic intrusion into the species of homo sapiens. It has been documented from both indirect measures understanding of the observed action motor cognition hypothesisThe results of Iacoboni et al. affordance when the child attempts to grasp the object for a spontaneous action or for imitation. Hence, in our experimental design, we introduced some tasks requiring the imitation of conventional actions with objects and contrasted them with other tasks that required either imitation of conventional actions without objects or of unconventional actions with objects. Accordingly, we set up an experimental design tailored at exploring, in the same child, some of the components that constitute the basic architecture of motor cognition: action imitation, self-generation and comprehension of pantomimes. We expect that an integrated analysis of these three domains of motor cognition may eventually provide a better understanding of the core mechanisms of autism.It is unlikely, though, that autism can be exclusively reduced to a deficit of motor cognition. Even less likely autism can be simply equated to a mere absence of the MNM. Both views appear too simplistic and fall short of capturing the multi-layered and diversified aspects characterizing ASD. Indeed, there is clear evidence that failure of ASD individuals in imitation of transitive actions extends beyond mere motor-executive aspects, by involving non-imitative aspects as well All of the children participating to this study completed the set of the presented tasks that had been previously approved by the local ethical committee (Comitato Etico Unico per la Provincia di Parma) and were conducted according to the Helsinki declaration. Written informed consent was obtained from the parents of each child involved in our study.We examined a group of fifteen high functioning children  with autism or autism spectrum disorder recruited from a Regional Centre for Autism in Northern Italy. All patients were free from any evident neurological abnormalities as well as from hearing or visual impairment. On the Wechsler Intelligence Scale for Children-Revised (WISC-R) The diagnosis was made according to the clinical criteria of DSM-IV We selected two control samples of TD children: the first one matched for chronological age (CA) and the second one of a chronologically younger age (CY). The first sample comprised 21 children from primary schools with a mean age of 8.7 years (SD 0.3); the second sample included 42 TD children from kindergarten . TD controls had no history of neurological or psychiatric diagnosis and were recruited from local primary and maternal school, according to a balanced match between males and females. Their non-verbal cognitive level, assessed by means of the Raven’s Progressive Matrices We set up three sets of tasks for the assessment of imitation, self-generation and comprehension of actions, respectively; the whole project comprised 9 different tasks. Each child was required to inspect a digitized video-clip and to perform (or recognize) an action according to the received instructions.The battery consisted of six tasks, each comprising 15 items.imitation of conventional actions with an object: the child watches an actor making an action with an object (e.g. drinking from a glass), then the child is required to repeat the observed action with a real object placed on the table, immediately after viewing the video clip.imitation of conventional actions without objects: the actor mimes an action without any object (e.g.: acting as if drinking from a glass without the glass), then the child is asked to repeat the action without the object.imitation of non conventional actions with an object: the child watches an actor making a non conventional action with an object (for example: turn a book around with the elbow), the child is asked to do the same.imitation of finger position: the items are performed with one or both hands; the child observes on the screen a particular hand posture being produced by the examiner with either one or both hands (e.g. extending or flexing one or more fingers); then the child is required to repeat the observed finger position.imitation of oro-facial gestures devoid of emotional content: the child watches an actor making a facial grimace, or protruding the tongue, then the child is required to repeat the observed gesture.The battery consisted of two tasks, each comprising 15 items.production of a pantomime from the picture of a tool: the child watches the drawing of an object/tool on the screen and then he/she is required to show how the real object/tool is commonly used .production of a pantomime from the oral naming of a tool: the actor says the name of an object/tool and the child is required to show how the object/tool is commonly used (e.g.: the actor says “glass” and the child has to mime drinking from a glass).The battery comprised two tasks, each one including 15 items.Visual Comprehension of Pantomimes: an actor mimes an action without the object; soon afterwards, the child views on the screen three pictures representing three tools (one target and two foils) and is required to point to the target picture, related to the mimed action.Oral Comprehension of Pantomimes: an actor says the name of an object/tool and mimes an action either representing the correct use of the object/tool or an action which is unrelated to the object/tool; the child is required to judge whether the mimed action corresponds correctly to the named object.The order of presentation of the tasks was counterbalanced across participants and the responses were video-recorded for further inspection. No time constraints were imposed, although we recorded the total time for each task.We rated the children’s actions according to the scoring criteria outlined by Watkins, Dronkers & Vargha-Khadem r = .0825 (p<.000) for the imitation of conventional actions with objects; for all of the remaining 6 motor tasks the r score ranges from.0928 (p<.000) to.9887 (p<.000).In order to obtain a reliability measure of the scoring procedure for the imitation and production tasks, the children’s performances were rated by two independent clinical psychologists. Inter-rater reliability was then calculated: the findings show a Pearson correlation score of We will present our findings from the nine batteries by clustering the results around the domains of imitation, production and comprehension of pantomimes. For each domain we will systematically compare ASD children with the two control samples of TD children: a chronological age-matched (CA) and a younger sample (CY), respectively. In particular, we will first compare imitation of conventional actions with objects vs. conventional actions without objects; our aim is to explore, in ASD children, whether the context might play a facilitating role in imitation, analogous to the positive effect of context on intention understanding Even a cursory glance at the findings makes it clear that neither a normal level of intelligence nor an advantage of four years discrepancy (with the younger sample), suffices for the Autistic children to outperform even their younger siblings in any of the motor tasks they were presented with.vs. 41.47 -SD 2.50) and in the CA sample (mean correct 44.35 -SD 1.61- vs. 43.05 -SD 1.04)  in the three groups (between-subject variables) by means of a mixed-model design ANOVA. The results indicate that Group (ASD and the two TD groups) and Task (with and without objects) as main factors, are highly significant: . Likewise significant is the interaction of Group by Task . In order to provide a systematic comparison of the degree of efficiency of the three samples for each one of the two types of tasks, we also run a Univariate ANOVA: the results show that the differences in the Imitation with objects is highly significant  across the three groups; similarly significant are the differences in the imitation task without objects . Interestingly, a post hoc test (Bonferroni), indicates that the ASD children achieve significantly lower levels of performance when compared with both the younger and the age-matched controls in the imitation with object (p<.0001) as well as in the imitation without object (p<.0001). Furthermore, for the ASD children, the relevance of the objects in the imitation tasks is plainly attested by the degree of the within-sample discrepancy between the imitation task with objects (mean correct: 42 -SD 2.61-) and without objects (mean correct: 30.26 -SD 5.89-). A quite different picture emerges from the within sample comparison between the two tasks in the CY children (mean correct 43.35 -SD 1.02- SD 1.04)  for the vs. non-conventional actions with objects) as main factors, indicates that both factors are highly significant: ; likewise significant is the interaction of Group by Task . A Univariate ANOVA show that the differences in the imitation of conventional actions is significant  across the three groups; similarly significant are the differences in the imitation of non conventional actions . A post-hoc test (Bonferroni) indicates that the ASD children fare at a significantly lower level with respect to both the CA controls (p<.0001) and the CY controls (p<.0001).When the ASD children are required to imitate non-conventional actions with objects, the need for planning a new  action again penalizes their performances. A mixed-model design ANOVA, with Group (ASD and the two TD control groups) and Task  further corroborate the previous findings . A mixedIn the two tasks requiring the production of pantomimes  the discrepancy between the ASD children and the TD control samples remains remarkable . A mixedIn the picture-pantomime matching task, the child is required to select a picture corresponding to the observed pantomime; in the word-pantomime matching task, the child is required to judge whether or not the heard word corresponds to the observed pantomime. As evident from Overall, our findings highlight two key aspects: 1) in ASD children the inefficiency of motor cognition is not restricted to imitation, but it cuts across the domains of both production and comprehension of pantomimes; 2) the accuracy of performance in each motor domain can be either worsened or improved, owing to the availability for ASD children of pre-existing motor plans, cued by a facilitating pragmatic context. Clearly, normal intelligence and chronological age advantage do not suffice for ASD children to achieve levels of performance equating even younger TD controls.Let us first consider the imitation of conventional actions: when the ASD children are allowed the use of objects, they obtain a mean score of 42 , but their performances collapse to a low mean score of 30,2  when the imitation of the same actions has to be performed without the use of the actual object. These performances are well below the level of even the younger  control sample, who obtain a mean score of 43,35  and 41,47  in the former and the latter task, respectively. Uneven performances of this magnitude suggest that ASD children find it particularly difficult to select a proper motor plan, when it requires a context-independent feed-forward mode of motor control. It is as if the required action had to be planned anew and the access to the stored motor templates was denied. In this condition, with the context playing no supportive role, ASD children are left with a motor task that is clearly far beyond their possibilities.vs. grasping), but fail in understanding the “why” of the same action  unless the action is embedded in an environmental context providing external cues to decipher the intention of the observed action. Our two present tasks, requiring the comprehension of pantomimes, reveal that ASD children fail even to grasp the “what” of a pantomime, when the contextual cues are less favourable (as when required to judge the correspondence between an observed pantomime and the heard name of a tool): the statistical analyses confirm that ASD fare significantly worse than their control samples.The relevance of a favourable external cue can be detected even in the comprehension of actions. In a previous study A further support to the widespread inefficiency of motor cognition in ASD children is provided by two additional imitation tasks: when our ASD children are required to imitate either oro-facial gestures or non-symbolic finger positions, their performance falls significantly below the accuracy level of TD samples.It should be noticed, though, that the interpretation of these findings is far from being unproblematic, as a number of experiments failed to document an impairment of imitation in ASD In one of the papers holding a critical position toward a “motor account”, the Authors assert that imitation disorders in ASD “may be driven by a lack of attention, or motor sequencing impairments” Furthermore, as evident from other studies, when attention is carefully controlled in imitation tasks with very young ASD children, it appears that “there [is] no evidence that either motor or attentional aspects of the tasks contributed to the poorer imitative performance of the children with autism” An overview of the findings from our study allows some speculations on the role of motor cognition in ASD children. In particular, the concomitant impairment of both production and “comprehension” of actions is suggestive of an early damage to a core component implementing both sides of action representation. We suggest that one such component is represented by the MNM that provides the neural substrate for matching action perception and action execution.before the execution thereof. Participants to this study were required both to execute and observe two different actions: a) grasping with the right hand a food item placed on a plate, bringing it into the mouth and eating it; b) grasping a piece of paper placed on the same plate and putting it into a box. During the execution and observation conditions of both actions the activity of the mouth-opening mylohyoid muscle (MH) of the participants was recorded using EMG surface electrodes. The results showed that during the execution and observation of the eating action, a sharp increase of MH activity was recorded in TD children, starting well before the food was grasped. No increase of MH activity was present during the observation of the placing action. This means that one of the muscles responsible for the action final goal (opening the mouth to eat a piece of food) is already activated during the initial phases of the action. The motor system anticipates the final goal of the action (to eat), thus directly mapping the action intention, both when the action is executed, or it is observed when performed by others. In contrast with TD children, ASD children showed a much later activation of the MH muscle during eating action execution and no activation at all during eating action observation.The theoretical relevance of the role played by the MNM in ASD can be fully appreciated when we consider a series of recent experiments that shed new light on the mechanisms that control both execution and pre-cognitive comprehension of motor acts in ASD children. An EMG experiment The results from Cattaneo et al. Further aspects of the motor domains, such as action simulation, mimicry and imitation, have been recently explored by a number of studies, all confirming a deep impairment of the core mechanisms of motor cognition in ASD children. Théoret and colleagues However, it has been shown that mu attenuation is sensitive to the degree of familiarity between agent and observer: when a sibling or someone related with the ASD individuals performed the observed action, mu attenuation could be detected The simultaneous impairment of action imitation, production and comprehension of pantomimes, as revealed by the present data, suggest that the process of constructing an action motor representation is deeply impaired in ASD children. Interestingly, the neuro-functional correlates of tool use pantomimes are associated with the activation of the left intra-parietal cortex and dorsolateral frontal cortex Overall, the wide range of deficits shown by ASD children in different motor domains, converge toward an impairment of a basic neurophysiologic mechanism that allows children  to “seeing others as a template of the self “


Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses. It is generally believed that our memories are stored in the synaptic connections between neurons. Numerous experimental studies have therefore examined when and how the synaptic connections change. In parallel, many computational studies have examined the properties of memory and synaptic plasticity, aiming to better understand human memory and allow for neural network models of the brain. However, the plasticity rules used in most studies are highly simplified and do not take into account the rich behaviour found in experiments. For instance, it has been observed in experiments that it is hard to make strong synapses even stronger. Here we show that this saturation of plasticity enhances the number of memories that can be stored and introduce a general framework to calculate information storage in online learning paradigms. Long term synaptic plasticity has been established as one of the most important components for learning and memory. In parallel with experimental investigations, numerous computational models of synaptic plasticity have been developed to simulate network function and to establish the fundamental characteristics and limitations of plasticity. Despite the complexity of the underlying neurobiology, theoretical studies have in the interest of tractability mostly focused on highly simplified plasticity rules One such experimental finding is that strong synapses are harder to potentiate than weak ones, that is, the percentage increase in strength is significantly smaller for strong synapses than for weak synapses Soft-bound plasticity automatically constrains the synaptic weights, and thereby resolves simply, but effectively, the danger of unconstrained plasticity, namely that on repeated activation, synaptic strength would grow indefinitely. In many modeling studies weight dependence is ignored, instead hard-bounds are typically introduced that cap the minimal and maximal synaptic weights , which are often supplemented with constraints on the total weight discrete synapses it has been suggested that soft-bounds fundamentally limit memory lifetime Despite the experimental evidence for soft-bound plasticity rules, the effect of weight dependence on information storage is not well understood To understand how different plasticity rules determine information capacity, we consider a simple, single neuron learning paradigm. The setup is shown in The task of the neuron is to recognize the patterns that it has encountered previously. To measure the performance we periodically interrupt the learning and test the neuron with both previously presented patterns , lifts the postsynaptic activity above a certain plasticity threshold. Either mechanism would slow down the learning and forgetting equally, but would not otherwise change our analysis weight distribution . Indeed, simulations with and without this Gaussian assumption gave virtually identical results. Using the Gaussian approximation, a signal to noise ratio (SNR) can be used to characterize the difference in the response between patterns and lures. With Because the neuron sums many inputs, the output distribution is well approximated with a Gaussian distribution. This holds independently of the In many storage capacity studies weights are initialized to zero, the number of items to be learned is fixed and learning stops after all the items have been presented, or once the task as been learned The central question we address is which plasticity rules lead to the best performance. We focus on the effect of the weight dependence and restrict ourselves to plasticity rules that are local (only dependent on the pre- and post-synaptic activity at that synapse) and incremental (have no access to the patterns presented earlier). We implemented first two plasticity rules, a hard-bound and a soft-bound one.For the hard-bound plasticity rule, potentiation occurs when the input relative amount of plasticity is typically reported. It was found that relative amount of depression is approximately constant , while the observed synaptic weight distributions are relatively broad, consistent with larger updates.We therefore define a second storage measure to compare hard and soft-bounds. We use the same single neuron paradigm used above but define the memory lifetime as the number of recent patterns that the neuron stores with SNR above a predefined threshold Because the SNR decays exponentially with soft-bound plasticity (Models), The lifetime in the hard-bound case can be approximated by taking only the lowest order term in the expression for the SNR decay (see Models), yieldingThe theory is confirmed by the simulations in which we numerically maximized the lifetime by changing the synaptic update. Too small updates would lead to none or few patterns above the threshold, while too large updates speed up the decay. The lifetime is plotted normalized by the number of synapses. The soft-bound plasticity, as well as the other soft-bound variants, outperform hard-bound, As indicated, the SNR decay leads to a trade-off between initial SNR and memory decay time. The current performance measure sets a somewhat arbitrary threshold to resolve this. However, the result is independent of the precise threshold setting. In the simulations and the analysis we made two assumptions. First, the input patterns take positive and negative values, but in biological systems it would seem more natural to assume that the inputs are spikes, that is, 1s and 0s. Secondly, we tacitly used fixed feedforward inhibition to balance the excitatory inputs and ensure that the effective synaptic weight has zero mean. Without these assumptions the SNR, and hence the information capacity, for both hard- and soft-bound plasticity decreases significantly.The reason for the decreased SNR is easiest seen by analyzing the variance in the output in response to a lure pattern. Because a lure pattern will be completely uncorrelated to the weights, the variance can be written asIn the above results the second term in The third term in In this idealized setup, feed-forward inhibition is mathematically equivalent to making the mean weight zero by allowing negative weights and adjusting the plasticity rules. For instance, for the hard-bound plasticity, this is achieved by just moving the lower weights bound to −1; for soft-bound plasticity, the depression rule is redefined to A further potential problem with hard-bound plasticity is that highest capacity is only attained when potentiation and depression are exactly balanced The width of the peak around We have studied plasticity rules that include the experimental observation that plasticity depends on the synaptic weight itself. Namely, the relative amount of potentiation decreases as the synapse gets stronger, while the relative amount of depression shows no such dependence. This means that the synaptic weight is automatically bounded. Using an information theoretic framework we have compared these plasticity rules to hard-bound plasticity in an online learning setting. We found that the information storage is higher for soft-bound plasticity rules. Contrasting the prototypical soft-bound and hard-bound plasticity rules, the improvement can be calculated analytically. In addition, a wide class of soft-bound plasticity rules lead to the same increased capacity and we suggest that soft-bound rules in fact maximize the synaptic capacity. Furthermore, we examined an alternative capacity measure that determines how many patterns can be stored above a certain threshold. This memory lifetime measure also improved for soft-bound plasticity.a priori there was no reason to assume that weight dependence would actually improve capacity, it could have led to a lower capacity. Moreover, naively one might have guessed, errorneously, that the uniform weight distribution associated with hard-bound plasticity would be optimal. The difference in capacity means that for hard-bound plasticity to perform as well as the soft-bound one, 18% more synapses would be required. This is a significant space and metabolic cost for the nervous system as synapses consume about 60% of the brain's energy The improvement in performance is moderate for both capacity measures, some 18%. However, it should be stressed that Given that the difference between hard and soft-bound is only quantitative, it is difficult to point to the cause for the difference, besides our mathematical derivation. Our intuition is that the hard-bounds lead to a deformation of the weight distribution. As a result, the decay back to equilibrium is a sum of exponentials. This means that the decay is always somewhat faster than for the soft-bound case for identical initial SNR, Our study reveals the following criteria to optimize synaptic storage capacity: 1) use soft-bound rules, 2) ensure that the mean input is close to zero, for instance by using sparse codes, 3) ensure that the mean weight is close to zero, for instance by implementing balanced feed-forward inhibition, and finally, if one wants to maximize the mutual information, 4) update the synapse in small amounts.There have recently been a number of studies on information storage and forgetting in synapses with a discrete number of states. The continuous synapses studied here have an equal or superior capacity, as they can always effectively act as discrete ones. An earlier study of discrete synapses argued that balanced hard-bound rules are superior to soft-bound rules when long memory lifetime is required independent plasticity would be difficult to achieve bio-physically, as many biophysical signals such as Ca influx and AMPA insertion are likely affected by the synaptic weight itself. Interestingly, the spine volume grows substantially as the synapse undergoes potentiation The results do not depend on the precise form of the soft-bound plasticity rules. Biophysically, numerous mechanisms with any kind of saturation, could lead to soft-bound plasticity. At the level of a single synapse one could even argue that weight-Apart from the increased capacity, weight dependence has other important consequences for plasticity. First, the weight dependence leads to central weight distributions, consistent with data, both measured electro-physiologically and microscopically. Second, competition between synapses is weaker for soft-bound rules because depressed synapses never completely disappear. Finally, in soft-bound plasticity the mean weight remains sensitive to correlations In an earlier study we reported that soft-bound STDP plasticity leads to shorter retention times than hard-bound plasticity in both single neurons and networks Soft-bound plasticity is certainly not the only way to prevent run-away plasticity. Apart from hard-bounds, BCM theory In this section we calculate the capacity of soft and hard-bound plasticity analytically. To calculate the capacity we concentrate on one single synapse, as the Signal-to-Noise scales linearly with the number of synapses. We artificially distinguish the pattern that is to be learned from all the other patterns that are presented subsequently and erase the memory of this pattern, although of course, no pattern stands above the others; the same plasticity rules underlie both the storage and the forgetting processes. Throughout we assume that the synaptic updates are small. This prevents saturation in the relation between SNR and Information, To calculate the information storage we need to study how the synaptic weight decays after learning. As the weight updates are small, a Fokker-Planck equation for the weight distribution describes the decay of the synapse as it is subject to the learning of other patterns. The synaptic weight distribution We first calculate the information capacity for the soft-bound rule. The solution Fokker-Planck is complicated, but when the jumps are much smaller than the standard deviation of the distribution, that is, We consider what happens to a given synapse when a pattern is learned. Suppose that at time zero, the synapse gets a high input and is thus potentiated . Right after the potentiation event, the weight distribution is displaced with an amount After this event, the synaptic weight is subject to random potentiation and depression events caused by the learning of patterns presented subsequently. During this the perturbation of the synaptic weight distribution will decay, until finally the distribution equals the equilibrium again and the potentiation event will have lost its trace. The perturbed distribution can be plugged into the Fokker-Planck equation to study its decay back to equilibrium. Because The output signal is found by probing the synapse with a ‘1’ (high input). Assuming perfectly tuned feed-forward inhibition, the mean output signal is In the simulations we found the same information for other soft-bound plasticity rules. For small updates, a Taylor expansion of the drift We repeat the calculation for hard-bound plasticity. We impose hard-bounds at At time As above, the synaptic weight is subject to random potentiation and depression events caused by the learning of patterns presented subsequently. Solving the Fokker-Planck equation gives that the perturbation of the synaptic weight distribution decays asThe derivative of the mean weight follows from the Fokker-Planck equation after integration by parts asThe variance in the output equals that of the uniform distribution and is For the soft-bound plasticity, the ratio between potentiation and depression determines the mean synaptic weight, but the capacity does not depend on it. For hard-bound plasticity, however, the situation is more complicated. Imbalance between potentiation and depression leads to a deformation of the steady state distribution and speeds up the decay after perturbations, changing the capacity.We assume that the size of the potentiation event is The computer code is made available on the first author's website. To examine the various plasticity rules, we presented typically one million patterns to the neuron, one pattern at every time-step. Every pattern led to an update of the synaptic weights according to the plasticity rules. After each time-step, the output of the neuron in response to this pattern, to its predecessors, as well as to lures was measured. The mean and the variance of the neuron's output were collected online as a function of the pattern's age, that is how many time-steps ago the pattern was presented. From this the SNR and information was calculated at the end of the simulation using Importantly, unlike the analysis above, the simulations are not restricted to small synaptic updates. Furthermore, the Gaussian assumption can be dropped in the simulations. In that case all the neuron's responses were stored, and the information was calculated at the end of the simulation using the full response distributions to patterns and lures. We found results were the virtually identical. It required, however, much more computer memory and time than the first method.Unless indicated otherwise, for the figures we used


The central circadian pacemaker in the suprachiasmatic nucleus (SCN) is a critical component of the neuroendocrine circuit controlling gonadotropin secretion from the pituitary gland. The SCN conveys photic information to hypothalamic targets including the gonadotropin releasing hormone neurons. Many of these target cells are also cell autonomous clocks. It has been suggested that, rather then being singularly driven by the SCN, the timing of gonadotropin secretion depends on the activity of multiple hypothalamic oscillators. While this view provides a novel twist to an old story, it does little to diminish the central role of rhythmic hypothalamic output in this system. It is now clear that the pituitary, ovary, uterus, and oviduct have functional molecular clocks. Evidence supports the notion that the clocks in these tissues contribute to the timing of events in reproductive physiology. The aim of this review is to highlight the current evidence for molecular clock function in the peripheral components of the female hypothalamo-pituitary-gonadal axis as it relates to the timing of gonadotropin secretion, ovulation, and parturition. Generating a complete picture of the timing systems role in pregnancy and parturition requires examining the molecular clocks role in the timing of events that precede fertilization. The temporal control of ovulation and the events that follow depend in large part on the timing of luteinizing hormone (LH) and follicle stimulating hormone (FSH) secretion from the pituitary gland . Serum gbmal1 and the repressors period  and cryptochrome   . In addi,2 . In ame  –23]. Howme  , 25. Theme  –30. Clocme  –33. Otheme  –36. Mutame  , 38 and me  .Taken together, these data indicate that while oscillators in the basal hypothalamus play a critical role, the peripheral components of the hypothalamo-pituitary-gonadal (HPG) axis may also contribute to the timing of reproductive physiology. Disruption of the molecular clock in these peripheral tissues or reduced synchrony amongst these oscillators may be a factor in diseases that cause infertility . The goaper1 expression in gonadotroph cell lines  through MAP kinase-dependent signaling  also leads to activation of per1 expression , and rev-erbα mRNA expression in the heart, liver, and kidney , and rev-erb in the oviduct, supporting the notion that the embryo is exposed to rhythmic environmental conditions during passage to the uterus , investigators have begun to more thoroughly and intensively characterize molecular clock function in the uterus and pituitary gland. Extension of this approach to the ovary and oviduct will provide a more complete picture of clock function in these tissues.It is clear that both adequate output of the SCN and coordination between the central pacemaker and peripheral clocks is critical for physiological homeostasis. Nowhere does this appear to be more true than in the reproductive axis. Rather than simply top–down hierarchical control, the HPG axis can be considered a partnership of synchronized and cooperative oscillators Figure . The meaThe author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


All the samples in NanoMiner have been annotated, preprocessed and normalized using standard methods that ensure the quality of the data analyses and enable the users to utilize the database systematically across the different experimental setups and platforms. With NanoMiner it is possible to 1) search and plot the expression profiles of one or several genes of interest, 2) cluster the samples within the datasets, 3) find differentially expressed genes in various nanoparticle studies, 4) detect the nanoparticles causing differential expression of selected genes, 5) analyze enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms for the detected genes and 6) search the expression values and differential expressions of the genes belonging to a specific KEGG pathway or Gene Ontology. In sum, NanoMiner database is a valuable collection of microarray data which can be also used as a data repository for future analyses.The potential impact of nanoparticles on the environment and on human health has attracted considerable interest worldwide. The amount of transcriptomics data, in which tissues and cell lines are exposed to nanoparticles, increases year by year. In addition to the importance of the original findings, this data can have value in broader context when combined with other previously acquired and published results. In order to facilitate the efficient usage of the data, we have developed the NanoMiner web resource ( Their applications cover both engineering and biomedical sectors (e.g. in drug delivery and gene therapy). There are more than 100,000 ENs with differences in their shape, size, surface and chemical composition Engineered nanoparticles (ENs) have been specifically manufactured to be incorporated into a product or process, in vitro and in vivo experimental systems Numerous recent studies have reported a variety of biological and toxicological interactions of ENs in in vitro studies is extracted from these databases and processed consistently across each dataset facilitating data access, exploration, and retrieval, as well as comparison between different studies. In addition, NanoMiner provides links to the original studies and an access to the annotations of the data samples. NanoMiner also has various visualization and statistical analysis options to aid nanoparticle research. With the wide selection of its data analysis and illustration options, NanoMiner is a unique tool for researchers working in toxicogenomics, which can be used, for example, to anticipate the outcome of the interaction of nanoparticles with biological systems and thus the future risk of using these materials.As an initial step to reach this goal, we have developed NanoMiner, a database containing experimental results from different nanoparticle related gene expression microarray studies. In the public databases such as Gene Expression Omnibus (GEO) http://www.ncbi.nlm.nih.gov/geo/) http://www.ebi.ac.uk/arrayexpress/) The NanoMiner database includes 404 samples of gene expression data from various human cell types exposed to nanoparticles. The datasets in NanoMiner originated from Gene Expression Omnibus  http://www.ebi.ac.uk/arrayexpress/) The datasets in NanoMiner were downloaded as raw data files from Gene Expression Omnibus  are clustered using correlation distances with average linkage. The columns represent the samples. The red and blue colors denote high and low expression values, respectively. In the boxplots, the top and bottom sides of the box represent the upper and lower quartiles (75th and 25th percentiles) respectively, while the band within the box represents the median (50th percentile). The whiskers represent the most extreme data points, and n is the total number of samples within each group. In every profiling option of NanoMiner, all the gene values can be downloaded for further use if needed.The R package limmaLinear modeling and empirical Bayes methods, which were implemented in the R package GOStats package KEGG.db which are used in the analysis utilize the hypergeometric distribution to obtain enriched ontologies and pathways. By default, all human ENSEMBL genes are used as a background gene group and the categories with a p-value ≤0.05 are considered significantly enriched.Enrichment analysis is a tool to find enriched GO terms or KEGG pathways for a given gene set. The NanoMiner is developed using CakePHP and MySQL on the Linux platform with Apache as the web server . Its webnanominer@cs.tut.fi. In addition, NanoMiner will be developed to follow the new standards and requirements of the field based on the suggestions from the users and the scientific community.As transcriptomics experiments are highly informative in nanotoxicological studies, there will be many new studies available in the future. We thus intend to update NanoMiner regularly to include the data of the latest studies. Users can suggest new data to be added into NanoMiner under the “Feedbacks” section or by directly contacting us at NanoMiner is a unique and urgently needed database of transcriptomics data gathered in the nanosafety research field containing hundreds of samples of nanoparticle-exposed cells analyzed by a variety of microarray platforms. With the several analysis and visualization options, NanoMiner can systematically reveal the effects of various nanoparticle exposures on the levels of gene expression, KEGG pathways and Gene Ontologies. NanoMiner is the first database that can be used to directly address the questions raised by the users, including: which are the genes whose expression values are statistically significantly changed during nanoparticle exposures for various types of cells, and which are the nanoparticles that have an effect on the expression of a specific gene. To obtain answers to these questions without NanoMiner, it would be compulsory to go through every study one by one and to accept the fact that all the studies could have been preprocessed, normalized and analyzed with different methods, which in turn will cause variation in the results and possibly convey to misleading interpretations. With the systematic annotation and analysis in NanoMiner, nanoparticle related transcriptomics research becomes much more feasible and straightforward. NanoMiner has been designed to be intuitive and easy to use, which makes utilization of the data fast and simple. The database is an important step towards harmonization of the information gathered in nanoparticle research.http://nanominer.cs.tut.fi/) is freely available upon registration for academic researchers using the service for noncommercial purposes. We anticipate that there will be new transcriptomics studies available in the future, and thus intend to regularly process and integrate the latest results into NanoMiner, in order to provide a durable resource for the nanomaterial research community.The NanoMiner web resource Click here for additional data file.Figure S2Illustration of the enriched KEGG P53 Signaling pathway. KEGG P53 Signaling pathway was enriched within the differentially expressed genes (DEGs) detected in HMDM sample set of GSE39330 in both timepoints 6 h and 24 h. The DEGs are highlighted in pink.(TIF)Click here for additional data file.Figure S3The system architecture of NanoMiner. The Web Browser sends requests to the Apache Web Server from the end users. Apache Web Server receives and processes the requests from the Web browser, loading the PHP scripts for the PHP scripting engine. The scripting engine parses and executes the scripts. PHP engine communicates with the MySQL database and fetches the data from the database. The database with MySQL management system executes the SQL statements and returns the query results, and then the web server materializes the results further and sends back to the end users.(TIF)Click here for additional data file.Table S1Annotation table of the samples in NanoMiner. The annotations include GEO ID/Array Express ID, Platform information, description of preprocessing and normalization methods, summary of the dataset, citation, pubmed ID, and the detailed information of the treatment i.e. timepoint, nanoparticle dose, and nanoparticle size.(XLS)Click here for additional data file.Table S2List of comparisons for which the differentially expressed gene lists are available. The table shows the information about the 85 comparisons that can be uploaded from NanoMiner, including the dataset ID, name of the comparison, number of genes in the array, information of the treated and control samples, and the cell type of the samples.(XLS)Click here for additional data file.Table S3Results of the differential expression analysis for the ZnO 10 µg reatments for the 6h and the 24h timepoints of HMDM samples within the data set GSE39330. Table shows the results returned from NanoMiner. Results include the logarithmic fold change of the gene values between the treatment and the control samples, average value across all the samples, p-value and adjusted p-value for the gene showing whether the difference in the gene value between the gene groups is statistically significant, and B-statistics that is a log odd value showing the probability that the gene is differentially expressed.(XLS)Click here for additional data file.Table S4The adjusted p-values of the genes within the GO:0006986 “response to unfolded protein” showing whether the gene is differentially expressed in the two comparisons of the ZnO treatments of HMDM samples within the data set GSE39330. By default, when the adjusted p-value ≤0.05 the gene is considered to be differentially expressed between the treated and control samples.(XLS)Click here for additional data file.Table S5The enrichment analysis results for the differentially expressed genes of GSE39330 HMDM samples. The genes differentially expressed  at both timepoints were input to the enrichment analysis, and the enrichments were computed through the KEGG pathways, GO Biological Processes, GO Molecular Functions, GO Cellular Components. The result gives the p-value showing whether the term is significantly enriched, expected count of the genes within the term, the odds-ratio of the genes of the list within the term, the total number of the genes and the genes found in the list within the term.(XLS)Click here for additional data file.Table S6The adjusted p-values of the gene-wise differential expression analysis for the genes within the GO:0006955: immune response. The value is marked in red, if the adjusted p-value ≤0.05 and the absolute fold change between the treated and control sample value ≥1.5.(XLS)Click here for additional data file.


This is the case of a twenty-two-year-old active duty male soldier with nonexertional chest pain and worsening performance on his physical fitness test. His history was significant for a diagnosis of dextrocardia upon entry to the military. On acute presentation to the emergency department, he was deemed a candidate for the expedited coronary computed tomographic angiography (CCTA) protocol to assess for a possible anatomic cause of his symptoms. CCTA revealed the presence of an anomalous right pulmonary vein draining into the inferior vena cava. Additionally, the imaging showed dextroversion of the heart, dilation of the inferior vena cava, right atrium, and right ventricle, as well as a hypoplastic right lung, a collection of findings consistent with scimitar syndrome and not dextrocardia. This is a case report of a twenty-two-year-old male soldier with nonexertional chest pain and worsening performance on his physical fitness test. His history was significant for a diagnosis of dextrocardia upon entry to the military. On physical examination, heart sounds were louder over the right hemithorax; there was a fixed split S2, and the point of maximal impulse was displaced to the right. He had normal lung sounds and no peripheral edema. An electrocardiogram was remarkable for left axis deviation of the p-wave, normal QRS axis, and a nonspecific intraventricular conduction delay. Because of his history of recurrent chest pain and vague history of dextrocardia not confirmed with ECG, patient underwent a cardiac computed tomographic angiography (CCTA) protocol to assess for a possible anatomic cause of his symptoms. CCTA, performed with sequential gating and a window of 40–80% to accommodate the large area under investigation, revealed the presence of an anomalous right pulmonary vein draining into the inferior vena cava. Additionally, the imaging showed dextroversion of the heart, dilation of the inferior vena cava, right atrium, and right ventricle, as well as a hypoplastic right lung, a collection of findings consistent with scimitar syndrome and not dextrocardia . There wReview of the patient's chest radiograph showed a classic scimitar sign, which is the presence of a scimitar-shaped pulmonary vein along the right cardiac border . Due to Scimitar syndrome is a rare form of anomalous pulmonary venous return with an estimated incidence of 2/100,000 in which all or part of the venous drainage from the right lung enters the inferior vena cava instead of the left atrium . PulmonaScimitar syndrome has classically been diagnosed in the cardiac catheterization laboratory, but CCTA—as part of a routine screening protocol in our institution for low-risk chest pain—was essential to make the diagnosis. It also demonstrated the right-sided pulmonary hypoplasia and dextroversion of the heart which are the most common abnormalities associated with scimitar syndrome. The patient ultimately proceeded to surgical correction of the anomalous pulmonary venous return, and CCTA was vital in operative planning and successful surgical repair. Scimitar syndrome is a rare variant of anomalous pulmonary venous return that is usually diagnosed in infanc, but may present in adults with symptoms of dyspnea on exertion, syncope, or heart failure. In this twenty-two-year-old soldier with prior combat deployments, CCTA was vital in the diagnosis of scimitar syndrome. This vascular anomaly and the associated congenital malformations likely led to the soldier's decreased exercise tolerance and were successfully repaired by a congenital heart specialist.