Flexibility of thought in high creative individuals represented by percolation analysis.

Flexibility of thought is theorized to play a critical role in the ability of high creative individuals to generate novel and innovative ideas. However, this has been examined only through indirect behavioral measures. Here we use network percolation analysis (removal of links in a network whose strength is below an increasing threshold) to computationally examine the robustness of the semantic memory networks of low and high creative individuals. Robustness of a network indicates its flexibility and thus can be used to quantify flexibility of thought as related to creativity. This is based on the assumption that the higher the robustness of the semantic network, the higher its flexibility. Our analysis reveals that the semantic network of high creative individuals is more robust to network percolation compared with the network of low creative individuals and that this higher robustness is related to differences in the structure of the networks. Specifically, we find that this higher robustness is related to stronger links connecting between different components of similar semantic words in the network, which may also help to facilitate spread of activation over their network. Thus, we directly and quantitatively examine the relation between flexibility of thought and creative ability. Our findings support the associative theory of creativity, which posits that high creative ability is related to a flexible structure of semantic memory. Finally, this approach may have further implications, by enabling a quantitative examination of flexibility of thought, in both healthy and clinical populations.

Molecular analysis and genotype-phenotype correlation of Diamond-Blackfan anemia.

Diamond-Blackfan anemia (DBA) features hypoplastic anemia and congenital malformations, largely caused by mutations in various ribosomal proteins. The aim of this study was to characterize the spectrum of genetic lesions causing DBA and identify genotypes that correlate with phenotypes of clinical significance. Seventy-four patients with DBA from across Canada were included. Nucleotide-level mutations or large deletions were identified in 10 ribosomal genes in 45 cases. The RPS19 mutation group was associated with higher requirement for chronic treatment for anemia than other DBA groups. Patients with RPS19 mutations, however, were more likely to maintain long-term corticosteroid response without requirement for further chronic transfusions. Conversely, patients with RPL11 mutations were less likely to need chronic treatment. Birth defects, including cardiac, skeletal, hand, cleft lip or palate and genitourinary malformations, also varied among the various genetic groups. Patients with RPS19 mutations had the fewest number of defects, while patients with RPL5 had the greatest number of birth defects. This is the first study to show differences between DBA genetic groups with regards to treatment. Previously unreported differences in the rate and types of birth defects were also identified. These data allow better patient counseling, a more personalized monitoring plan, and may also suggest differential functions of DBA genes on ribosome and extra-ribosomal functions.

Critical tipping point distinguishing two types of transitions in modular network structures.

Modularity is a key organizing principle in real-world large-scale complex networks. The relatively sparse interactions between modules are critical to the functionality of the system and are often the first to fail. We model such failures as site percolation targeting interconnected nodes, those connecting between modules. We find, using percolation theory and simulations, that they lead to a "tipping point" between two distinct regimes. In one regime, removal of interconnected nodes fragments the modules internally and causes the system to collapse. In contrast, in the other regime, while only attacking a small fraction of nodes, the modules remain but become disconnected, breaking the entire system. We show that networks with broader degree distribution might be highly vulnerable to such attacks since only few nodes are needed to interconnect the modules, consequently putting the entire system at high risk. Our model has the potential to shed light on many real-world phenomena, and we briefly consider its implications on recent advances in the understanding of several neurocognitive processes and diseases.

The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks.

Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic's importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.

Discovering social events through online attention.

Twitter is a major social media platform in which users send and read messages ("tweets") of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible "thermostats" of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

Semantic organization in children with cochlear implants: computational analysis of verbal fluency.

PURPOSE: Cochlear implants (CIs) enable children with severe and profound hearing impairments to perceive the sensation of sound sufficiently to permit oral language acquisition. So far, studies have focused mainly on technological improvements and general outcomes of implantation for speech perception and spoken language development. This study quantitatively explored the organization of the semantic networks of children with CIs in comparison to those of age-matched normal hearing (NH) peers. METHOD: Twenty seven children with CIs and twenty seven age- and IQ-matched NH children ages 7-10 were tested on a timed animal verbal fluency task (Name as many animals as you can). The responses were analyzed using correlation and network methodologies. The structure of the animal category semantic network for both groups were extracted and compared. RESULTS: Children with CIs appeared to have a less-developed semantic network structure compared to age-matched NH peers. The average shortest path length (ASPL) and the network diameter measures were larger for the NH group compared to the CIs group. This difference was consistent for the analysis of networks derived from animal names generated by each group [sample-matched correlation networks (SMCN)] and for the networks derived from the common animal names generated by both groups [word-matched correlation networks (WMCN)]. CONCLUSIONS: The main difference between the semantic networks of children with CIs and NH lies in the network structure. The semantic network of children with CIs is under-developed compared to the semantic network of the age-matched NH children. We discuss the practical and clinical implications of our findings.

How high frequency trading affects a market index.

The relationship between a market index and its constituent stocks is complicated. While an index is a weighted average of its constituent stocks, when the investigated time scale is one day or longer the index has been found to have a stronger effect on the stocks than vice versa. We explore how this interaction changes in short time scales using high frequency data. Using a correlation-based analysis approach, we find that in short time scales stocks have a stronger influence on the index. These findings have implications for high frequency trading and suggest that the price of an index should be published on shorter time scales, as close as possible to those of the actual transaction time scale.

Individual and meta-immune networks.

Networks can be found everywhere-in technology, in nature and in our bodies. In this paper we present how antigen networks can be used as a model to study network interaction and architecture. Utilizing antigen microarray data of the reactivity of hundreds of antibodies of sera of ten mothers and their newborns, we reconstruct networks, either isotype specific (IgM or IgG) or person specific-mothers or newborns-and investigate the network properties. Such an approach makes it possible to decipher fundamental information regarding the personal immune network state and its unique characteristics. In the current paper we demonstrate how we are successful in studying the interaction between two dependent networks, the maternal IgG repertoire and the one of the offspring, using the concept of meta-network provides essential information regarding the biological phenomenon of cross placental transfer. Such an approach is useful in the study of coupled networks in variety of scientific fields.

Quantifying the behavior of stock correlations under market stress.

Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios.

The competitiveness versus the wealth of a country.

Politicians world-wide frequently promise a better life for their citizens. We find that the probability that a country will increase its per capita GDP (gdp) rank within a decade follows an exponential distribution with decay constant λ = 0.12. We use the Corruption Perceptions Index (CPI) and the Global Competitiveness Index (GCI) and find that the distribution of change in CPI (GCI) rank follows exponential functions with approximately the same exponent as λ, suggesting that the dynamics of gdp, CPI, and GCI may share the same origin. Using the GCI, we develop a new measure, which we call relative competitiveness, to evaluate an economy's competitiveness relative to its gdp. For all European and EU countries during the 2008-2011 economic downturn we find that the drop in gdp in more competitve countries relative to gdp was substantially smaller than in relatively less competitive countries, which is valuable information for policymakers.

The natural autoantibody repertoire in newborns and adults: a current overview.

Antibody networks have been studied in the past based on the connectivity between idiotypes and anti-idiotypes-antibodies that bind one another. Here we call attention to a different network of antibodies, antibodies connected by their reactivities to sets of antigens-the antigen-reactivity network. The recent development of antigen microarray chip technology for detecting global patterns of antibody reactivities makes it possible to study the immune system quantitatively using network analysis tools. Here, we review the analyses of IgM and IgG autoantibody reactivities of sera of mothers and their offspring (umbilical cords) to 300 defined self-antigens; the autoantibody reactivities present in cord blood represent the natural autoimmune repertories with which healthy humans begin life and the mothers' reactivities reflect the development of the repertoires in healthy young adults. Comparing the cord and maternal reactivities using several analytic tools led to the following conclusions: (1) The IgG repertoires showed a high correlation between each mother and her newborn; the IgM repertoires of all the cords were very similar and each cord differed from its mother's IgM repertoire. Thus, different humans are born with very similar IgM autoantibodies produced in utero and with unique IgG autoantibodies found in their individual mothers. (2) Autoantibody repertoires appear to be structured into sets of reactivities that are organized into cliques-reactivities to particular antigens are correlated. (3) Autoantibody repertoires are organized as networks of reactivities in which certain key antigen reactivities dominate the network-the dominant antigen reactivities manifest a "causal" relationship to sets of other correlated reactivities. Thus, repertoires of autoantibodies in healthy subjects, the immunological homunculus, are structured in hierarchies of antigen reactivities.

Improving implementation of infection control guidelines to reduce nosocomial infection rates: pioneering the report card.

BACKGROUND: Two detailed checklists were developed, based on published infection control guidelines, for daily use by infection control practitioners in departments and operating rooms. AIM: To assess the impact of the checklists on nosocomial infection rates in three hospitals over the course of one year. METHODS: The checklists included 20 subheadings (± 150 items). Project nurses conducted rounds in the study (but not control) departments; during each round, the nurses selected 15-20 items for observation, marked the checklists according to appropriateness of observed behaviour and provided on-the-spot corrective education. Rates of adherence to the checklist, antibiotic use, number of obtained and positive cultures, and positive staff hand and patient environment cultures were reported monthly as a report card to relevant personnel and administrators. The rate of nosocomial infections was determined in the first and last months. RESULTS: The baseline nosocomial infection rate was similar in the study and control departments: 37/345 (11%) and 26/270 (10%) respectively. In the last month, the rate in the study department decreased to 16/383 (4%) (P<0.01); in the control it decreased insignificantly to 21/248 (8%) (not significant). No significant trends were detected in the number of obtained cultures, positive cultures, or antibiotic use. Adherence to guidelines ranged from 75% to 94% between the hospitals (P<0.001): the overall rate increased from 80% to 91% (P<0.01). CONCLUSIONS: The use of checklists during the conduct of infection control rounds, combined with monthly reports, was associated with a significant decrease in nosocomial infections in study departments.

Evolvement of uniformity and volatility in the stressed global financial village.

BACKGROUND: In the current era of strong worldwide market couplings the global financial village became highly prone to systemic collapses, events that can rapidly sweep throughout the entire village. METHODOLOGY/PRINCIPAL FINDINGS: We present a new methodology to assess and quantify inter-market relations. The approach is based on the correlations between the market index, the index volatility, the market Index Cohesive Force and the meta-correlations (correlations between the intra-correlations.) We investigated the relations between six important world markets--U.S., U.K., Germany, Japan, China and India--from January 2000 until December 2010. We found that while the developed "western" markets (U.S., U.K., Germany) are highly correlated, the interdependencies between these markets and the developing "eastern" markets (India and China) are volatile and with noticeable maxima at times of global world events. The Japanese market switches "identity"--it switches between periods of high meta-correlations with the "western" markets and periods when it behaves more similarly to the "eastern" markets. CONCLUSIONS/SIGNIFICANCE: The methodological framework presented here provides a way to quantify the evolvement of interdependencies in the global market, evaluate a world financial network and quantify changes in the world inter market relations. Such changes can be used as precursors to the agitation of the global financial village. Hence, the new approach can help to develop a sensitive "financial seismograph" to detect early signs of global financial crises so they can be treated before they develop into worldwide events.

Global and local features of semantic networks: evidence from the Hebrew mental lexicon.

BACKGROUND: Semantic memory has generated much research. As such, the majority of investigations have focused on the English language, and much less on other languages, such as Hebrew. Furthermore, little research has been done on search processes within the semantic network, even though they are abundant within cognitive semantic phenomena. METHODOLOGY/PRINCIPAL FINDINGS: We examine a unique dataset of free association norms to a set of target words and make use of correlation and network theory methodologies to investigate the global and local features of the Hebrew lexicon. The global features of the lexicon are investigated through the use of association correlations--correlations between target words, based on their association responses similarity; the local features of the lexicon are investigated through the use of association dependencies--the influence words have in the network on other words. CONCLUSIONS/SIGNIFICANCE: Our investigation uncovered Small-World Network features of the Hebrew lexicon, specifically a high clustering coefficient and a scale-free distribution, and provides means to examine how words group together into semantically related 'free categories'. Our novel approach enables us to identify how words facilitate or inhibit the spread of activation within the network, and how these words influence each other. We discuss how these properties relate to classical research on spreading activation and suggest that these properties influence cognitive semantic search processes. A semantic search task, the Remote Association Test is discussed in light of our findings.

Genetic analysis of inherited bone marrow failure syndromes from one prospective, comprehensive and population-based cohort and identification of novel mutations.

INTRODUCTION: Inherited bone marrow failure syndromes (IBMFSs) often have substantial phenotypic overlap, thus genotyping is often critical for establishing a diagnosis. OBJECTIVES AND METHODS: To determine the genetic characteristics and mutation profiles of IBMFSs, a comprehensive population-based study that prospectively enrols all typical and atypical cases without bias is required. The Canadian Inherited Marrow Failure Study is such a study, and was used to extract clinical and genetic information for patients enrolled up to May 2010. RESULTS: Among the 259 primary patients with IBMFS enrolled in the study, the most prevalent categories were Diamond-Blackfan anaemia (44 patients), Fanconi anaemia (39) and Shwachman-Diamond syndrome (35). The estimated incidence of the primary IBMFSs was 64.5 per 10(6) births, with Fanconi anaemia having the highest incidence (11.4 cases per 10(6) births). A large number of patients (70) had haematological and non-haematological features that did not fulfil the diagnostic criteria of any specific IBMFS category. Disease-causing mutations were identified in 53.5% of the 142 patients tested, and in 16 different genes. Ten novel mutations in SBDS, RPL5, FANCA, FANCG, MPL and G6PT were identified. The most common mutations were nonsense (31 alleles) and splice site (28). Genetic heterogeneity of most IBMFSs was evident; however, the most commonly mutated gene was SBDS, followed by FANCA and RPS19. CONCLUSION: From this the largest published comprehensive cohort of IBMFSs, it can be concluded that recent advances have led to successful genotyping of about half of the patients. Establishing a genetic diagnosis is still challenging and there is a critical need to develop novel diagnostic tools.

Index cohesive force analysis reveals that the US market became prone to systemic collapses since 2002.

BACKGROUND: The 2007-2009 financial crisis, and its fallout, has strongly emphasized the need to define new ways and measures to study and assess the stock market dynamics. METHODOLOGY/PRINCIPAL FINDINGS: The S&P500 dynamics during 4/1999-4/2010 is investigated in terms of the index cohesive force (ICF--the balance between the stock correlations and the partial correlations after subtraction of the index contribution), and the Eigenvalue entropy of the stock correlation matrices. We found a rapid market transition at the end of 2001 from a flexible state of low ICF into a stiff (nonflexible) state of high ICF that is prone to market systemic collapses. The stiff state is also marked by strong effect of the market index on the stock-stock correlations as well as bursts of high stock correlations reminiscence of epileptic brain activity. CONCLUSIONS/SIGNIFICANCE: The market dynamical states, stability and transition between economic states was studies using new quantitative measures. Doing so shed new light on the origin and nature of the current crisis. The new approach is likely to be applicable to other classes of complex systems from gene networks to the human brain.

Analyses of antigen dependency networks unveil immune system reorganization between birth and adulthood.

Much effort has been devoted to assess the importance of nodes in complex biological networks (such as gene transcriptional regulatory networks, protein interaction networks, and neural networks). Examples of commonly used measures of node importance include node degree, node centrality, and node vulnerability score (the effect of the node deletion on the network efficiency). Here, we present a new approach to compute and investigate the mutual dependencies between network nodes from the matrices of node-node correlations. To this end, we first define the dependency of node i on node j (or the influence of node j on node i), D(i, j) as the average over all nodes k of the difference between the i - k correlation and the partial correlations between these nodes with respect to node j. Note that the dependencies, D(i, j) define a directed weighted matrix, since, in general, D(i, j) differs from D( j, i). For this reason, many of the commonly used measures of node importance, such as node centrality, cannot be used. Hence, to assess the node importance of the dependency networks, we define the system level influence (SLI) of antigen j, SLI( j) as the sum of the influence of j on all other antigens i. Next, we define the system level influence or the influence score of antigen j, SLI( j) as the sum of D(i, j) over all nodes i. We introduce the new approach and demonstrate that it can unveil important biological information in the context of the immune system. More specifically, we investigated antigen dependency networks computed from antigen microarray data of autoantibody reactivity of IgM and IgG isotypes present in the sera of ten mothers and their newborns. We found that the analysis was able to unveil that there is only a subset of antigens that have high influence scores (SLI) common both to the mothers and newborns. Networks comparison in terms of modularity (using the Newman's algorithm) and of topology (measured by the divergence rate) revealed that, at birth, the IgG networks exhibit a more profound global reorganization while the IgM networks exhibit a more profound local reorganization. During immune system development, the modularity of the IgG network increases and becomes comparable to that of the IgM networks at adulthood. We also found the existence of several conserved IgG and IgM network motifs between the maternal and newborns networks, which might retain network information as our immune system develops. If correct, these findings provide a convincing demonstration of the effectiveness of the new approach to unveil most significant biological information. Whereas we have introduced the new approach within the context of the immune system, it is expected to be effective in the studies of other complex biological social, financial, and manmade networks.

Network theory analysis of antibody-antigen reactivity data: the immune trees at birth and adulthood.

MOTIVATION: New antigen microarray technology enables parallel recording of antibody reactivities with hundreds of antigens. Such data affords system level analysis of the immune system's organization using methods and approaches from network theory. Here we measured the reactivity of 290 antigens (for both the IgG and IgM isotypes) of 10 healthy mothers and their term newborns. We constructed antigen correlation networks (or immune networks) whose nodes are the antigens and the edges are the antigen-antigen reactivity correlations, and we also computed their corresponding minimum spanning trees (MST)--maximal information reduced sub-graphs. We quantify the network organization (topology) in terms of the network theory divergence rate measure and rank the antigen importance in the full antigen correlation networks by the eigen-value centrality measure. This analysis makes possible the characterization and comparison of the IgG and IgM immune networks at birth (newborns) and adulthood (mothers) in terms of topology and node importance. RESULTS: Comparison of the immune network topology at birth and adulthood revealed partial conservation of the IgG immune network topology, and significant reorganization of the IgM immune networks. Inspection of the antigen importance revealed some dominant (in terms of high centrality) antigens in the IgG and IgM networks at birth, which retain their importance at adulthood.