Mapping Interdisciplinary Fields: Efficiencies,Gaps and Redundancies in HIV/AIDS Research

While interdisciplinarity continues to increase in popularity among funders and other scientific organizations, its potential to promote scientific advances remains under-examined. For HIV/AIDS research, we examine the dynamics of disciplinary integration (or lack thereof) providing insight into a field''s knowledge base and those questions that remain unresolved. Drawing on the complete histories of two interdisciplinary journals, we construct bibliographic coupling networks based on overlapping citations to identify segregation into research clusters and estimate topic models of research content. We then compare how readily those bibliographic coupling clusters account for the structuring of topics covered within the field as it evolves over two decades. These comparisons challenge one-dimensional and/or cross-sectional approaches to interdisciplinarity. Some topics are increasingly coordinated across disciplinary boundaries (e.g., vaccine development); others remain relatively segmented into disconnected disciplinary domains for the full period (e.g., drug resistance). This divergence indicates heterogeneity in interdisciplinarity and emphasizes the need for critical approaches to studying the organization of science.     

Quantitative Analysis of the Interdisciplinarity of Applied Mathematics

The increasing use of mathematical techniques in scientific research leads to the interdisciplinarity of applied mathematics. This viewpoint is validated quantitatively here by statistical and network analysis on the corpus PNAS 1999–2013. A network describing the interdisciplinary relationships between disciplines in a panoramic view is built based on the corpus. Specific network indicators show the hub role of applied mathematics in interdisciplinary research. The statistical analysis on the corpus content finds that algorithms, a primary topic of applied mathematics, positively correlates, increasingly co-occurs, and has an equilibrium relationship in the long-run with certain typical research paradigms and methodologies. The finding can be understood as an intrinsic cause of the interdisciplinarity of applied mathematics.     

Web-Based Surveillance of Public Information Needs for Informing Preconception Interventions

Background

The risk of adverse pregnancy outcomes can be minimized through the adoption of healthy lifestyles before pregnancy by women of childbearing age. Initiatives for promotion of preconception health may be difficult to implement. Internet can be used to build tailored health interventions through identification of the public''s information needs. To this aim, we developed a semi-automatic web-based system for monitoring Google searches, web pages and activity on social networks, regarding preconception health.

Methods

Based on the American College of Obstetricians and Gynecologists guidelines and on the actual search behaviors of Italian Internet users, we defined a set of keywords targeting preconception care topics. Using these keywords, we analyzed the usage of Google search engine and identified web pages containing preconception care recommendations. We also monitored how the selected web pages were shared on social networks. We analyzed discrepancies between searched and published information and the sharing pattern of the topics.

Results

We identified 1,807 Google search queries which generated a total of 1,995,030 searches during the study period. Less than 10% of the reviewed pages contained preconception care information and in 42.8% information was consistent with ACOG guidelines. Facebook was the most used social network for sharing. Nutrition, Chronic Diseases and Infectious Diseases were the most published and searched topics. Regarding Genetic Risk and Folic Acid, a high search volume was not associated to a high web page production, while Medication pages were more frequently published than searched. Vaccinations elicited high sharing although web page production was low; this effect was quite variable in time.

Conclusion

Our study represent a resource to prioritize communication on specific topics on the web, to address misconceptions, and to tailor interventions to specific populations.     

TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics

Biological networks are a topic of great current interest, particularly with the publication of a number of large genome-wide interaction datasets. They are globally characterized by a variety of graph-theoretic statistics, such as the degree distribution, clustering coefficient, characteristic path length and diameter. Moreover, real protein networks are quite complex and can often be divided into many sub-networks through systematic selection of different nodes and edges. For instance, proteins can be sub-divided by expression level, length, amino-acid composition, solubility, secondary structure and function. A challenging research question is to compare the topologies of sub- networks, looking for global differences associated with different types of proteins. TopNet is an automated web tool designed to address this question, calculating and comparing topological characteristics for different sub-networks derived from any given protein network. It provides reasonable solutions to the calculation of network statistics for sub-networks embedded within a larger network and gives simplified views of a sub-network of interest, allowing one to navigate through it. After constructing TopNet, we applied it to the interaction networks and protein classes currently available for yeast. We were able to find a number of potential biological correlations. In particular, we found that soluble proteins had more interactions than membrane proteins. Moreover, amongst soluble proteins, those that were highly expressed, had many polar amino acids, and had many alpha helices, tended to have the most interaction partners. Interestingly, TopNet also turned up some systematic biases in the current yeast interaction network: on average, proteins with a known functional classification had many more interaction partners than those without. This phenomenon may reflect the incompleteness of the experimentally determined yeast interaction network.     

LabVelocity: online tools for life science products, protocols, technical information, MEDLINE searches, and laboratory calculations.

As the pace of life science discovery increases, so do the demands on researchers. To remain competitive in the life science industry, researchers must use every tool at their disposal to keep up with new products, protocols, news, and literature in their field. While there are now myriad Web sites that assist researchers with this problem, many suffer from confusing user interfaces, poorly designed search engines, and a narrow information focus. Here, we present LabVelocity, a user-friendly Web site that provides a free multidisciplinary information-gathering service for the life science research community. Using LabVelocity, a researcher can quickly find the products, protocols, technical references, news, MEDLINE abstracts, and interactive software tools necessary for an experiment. This aggregation of information can streamline experimental planning and is especially useful when researchers want to set up a new laboratory or to venture outside their field of expertise.     

The NIDDK Information Network: A Community Portal for Finding Data,Materials, and Tools for Researchers Studying Diabetes,Digestive, and Kidney Diseases

The NIDDK Information Network (dkNET; http://dknet.org) was launched to serve the needs of basic and clinical investigators in metabolic, digestive and kidney disease by facilitating access to research resources that advance the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). By research resources, we mean the multitude of data, software tools, materials, services, projects and organizations available to researchers in the public domain. Most of these are accessed via web-accessible databases or web portals, each developed, designed and maintained by numerous different projects, organizations and individuals. While many of the large government funded databases, maintained by agencies such as European Bioinformatics Institute and the National Center for Biotechnology Information, are well known to researchers, many more that have been developed by and for the biomedical research community are unknown or underutilized. At least part of the problem is the nature of dynamic databases, which are considered part of the “hidden” web, that is, content that is not easily accessed by search engines. dkNET was created specifically to address the challenge of connecting researchers to research resources via these types of community databases and web portals. dkNET functions as a “search engine for data”, searching across millions of database records contained in hundreds of biomedical databases developed and maintained by independent projects around the world. A primary focus of dkNET are centers and projects specifically created to provide high quality data and resources to NIDDK researchers. Through the novel data ingest process used in dkNET, additional data sources can easily be incorporated, allowing it to scale with the growth of digital data and the needs of the dkNET community. Here, we provide an overview of the dkNET portal and its functions. We show how dkNET can be used to address a variety of use cases that involve searching for research resources.     

Exploiting Publication Contents and Collaboration Networks for Collaborator Recommendation

Thanks to the proliferation of online social networks, it has become conventional for researchers to communicate and collaborate with each other. Meanwhile, one critical challenge arises, that is, how to find the most relevant and potential collaborators for each researcher? In this work, we propose a novel collaborator recommendation model called CCRec, which combines the information on researchers’ publications and collaboration network to generate better recommendation. In order to effectively identify the most potential collaborators for researchers, we adopt a topic clustering model to identify the academic domains, as well as a random walk model to compute researchers’ feature vectors. Using DBLP datasets, we conduct benchmarking experiments to examine the performance of CCRec. The experimental results show that CCRec outperforms other state-of-the-art methods in terms of precision, recall and F1 score.     

Owner controlled data exchange in nutrigenomic collaborations: the NuGO information network

New ‘omics’ technologies are changing nutritional sciences research. They enable to tackle increasingly complex questions but also increase the need for collaboration between research groups. An important challenge for successful collaboration is the management and structured exchange of information that accompanies data-intense technologies. NuGO, the European Nutrigenomics Organization, the major collaborating network in molecular nutritional sciences, is supporting the application of modern information technologies in this area. We have developed and implemented a concept for data management and computing infrastructure that supports collaboration between nutrigenomics researchers. The system fills the gap between “private” storing with occasional file sharing by email and the use of centralized databases. It provides flexible tools to share data, also during experiments, while preserving ownership. The NuGO Information Network is a decentral, distributed system for data exchange based on standard web technology. Secure access to data, maintained by the individual researcher, is enabled by web services based on the the BioMoby framework. A central directory provides information about available web services. The flexibility of the infrastructure allows a wide variety of services for data processing and integration by combining several web services, including public services. Therefore, this integrated information system is suited for other research collaborations.     

Does Human Migration Affect International Trade? A Complex-Network Perspective

This paper explores the relationships between international human migration and merchandise trade using a complex-network approach. We firstly compare the topological structure of worldwide networks of human migration and bilateral trade over the period 1960–2000. Next, we ask whether pairs of countries that are more central in the migration network trade more. We show that: (i) the networks of international migration and trade are strongly correlated, and such correlation can be mostly explained by country economic/demographic size and geographical distance; (ii) centrality in the international-migration network boosts bilateral trade; (iii) intensive forms of country centrality are more trade enhancing than their extensive counterparts. Our findings suggest that bilateral trade between any two countries is not only affected by the presence of migrants from either countries, but also by their relative embeddedness in the complex web of corridors making up the network of international human migration.     

How structure determines correlations in neuronal networks

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.     

Research, Informed Consent, and the Limits of Disclosure

According to this paper, respect for informed consent implies that subjects should often be told a good deal more than ethical guidelines explicitly or implicitly require. Unless subjects are informed of the researchers' personal characteristics, views, and sponsors whenever they would be likely to consider them significant, their autonomy is being overridden. However, overriding subjects' autonomy is sometimes required by the interests of researchers in not being discriminated against or suffering intrusions into their privacy. This paper resolves the conflict between informed consent and the interests of researchers by recommending that (i) subjects generally should be told of the personal characteristics of researchers when relevant as part of the researchers' job and (ii) that subjects should be told of researchers' views when conceptually connected to the research and (iii) that subjects should almost always be told about sponsorship. While the paper explicitly limits the role of informed consent, these recommendations go significantly beyond most guidelines in their requirements about what information should be disclosed.     

FINES,ORDERS, FEAR . . . AND CONSENT? MEDICAL RESEARCH IN EAST AFRICA,C. 1950s

This article reconstructs the history of medical research in East Africa (Kenya, Tanganyika, Uganda), laying out the lies, rumours, and oppressive techniques that made research such a fraught enterprise during the colonial era. The focus is on the beginning stages of medical research: researchers' arrivals, villagers' responses, the gathering of subjects and consent. New archival and oral sources gathered in East Africa illuminate the research encounter and reintegrate the perspective of villagers cum subjects. Data from the 1950s shows that upon arrival in a village, researchers regularly lied in order to avoid sensitive topics and sidestep potential opposition. Misinformation fuelled villagers' fears, skepticism and rumours of blood stealing researchers. When it came to gathering subjects, researchers were rarely involved in the challenging work of enticing villagers to participate, preferring to rely on chiefs. Chiefs, however, often relied on heavy-handed and ethically questionable techniques. The article concludes by looking at the much-discussed concept of group consent, and showing that historically a chief never had the authority to consent on behalf of villagers.     

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene–gene expression correlations are a common source of molecular networks because they can be extracted from high‐dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming ‘hub genes’ direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi‐omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease‐related prioritization of genes and molecular systems and network meta‐analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality–lethality relationship, integration with machine learning approaches and network pharmacology .     

esyN: Network Building,Sharing and Publishing

The construction and analysis of networks is increasingly widespread in biological research. We have developed esyN (“easy networks”) as a free and open source tool to facilitate the exchange of biological network models between researchers. esyN acts as a searchable database of user-created networks from any field. We have developed a simple companion web tool that enables users to view and edit networks using data from publicly available databases. Both normal interaction networks (graphs) and Petri nets can be created. In addition to its basic tools, esyN contains a number of logical templates that can be used to create models more easily. The ability to use previously published models as building blocks makes esyN a powerful tool for the construction of models and network graphs. Users are able to save their own projects online and share them either publicly or with a list of collaborators. The latter can be given the ability to edit the network themselves, allowing online collaboration on network construction. esyN is designed to facilitate unrestricted exchange of this increasingly important type of biological information. Ultimately, the aim of esyN is to bring the advantages of Open Source software development to the construction of biological networks.     

The association between four citation metrics and peer rankings of research influence of Australian researchers in six fields of public health

Doubt about the relevance, appropriateness and transparency of peer review has promoted the use of citation metrics as a viable adjunct or alternative in the assessment of research impact. It is also commonly acknowledged that research metrics will not replace peer review unless they are shown to correspond with the assessment of peers. This paper evaluates the relationship between researchers' influence as evaluated by their peers and various citation metrics representing different aspects of research output in 6 fields of public health in Australia. For four fields, the results showed a modest positive correlation between different research metrics and peer assessments of research influence. However, for two fields, tobacco and injury, negative or no correlations were found. This suggests a peer understanding of research influence within these fields differed from visibility in the mainstream, peer-reviewed scientific literature. This research therefore recommends the use of both peer review and metrics in a combined approach in assessing research influence. Future research evaluation frameworks intent on incorporating metrics should first analyse each field closely to determine what measures of research influence are valued highly by members of that research community. This will aid the development of comprehensive and relevant frameworks with which to fairly and transparently distribute research funds or approve promotion applications.     

AliBaba: PubMed as a graph

The biomedical literature contains a wealth of information on associations between many different types of objects, such as protein-protein interactions, gene-disease associations and subcellular locations of proteins. When searching such information using conventional search engines, e.g. PubMed, users see the data only one-abstract at a time and 'hidden' in natural language text. AliBaba is an interactive tool for graphical summarization of search results. It parses the set of abstracts that fit a PubMed query and presents extracted information on biomedical objects and their relationships as a graphical network. AliBaba extracts associations between cells, diseases, drugs, proteins, species and tissues. Several filter options allow for a more focused search. Thus, researchers can grasp complex networks described in various articles at a glance. AVAILABILITY: http://alibaba.informatik.hu-berlin.de/     

Proceedings of the fourth National Congress of the Italian Society of Virology

The aim of the yearly National Congress of the Italian Society of Virology (SIV) is to promote the discussion between senior and younger researchers to improve the knowledge and scientific collaboration among the various areas of Virology. The invited and selected lecturers of the fourth National Congress of SIV covered the following topics: general Virology and viral Genetics; virus host interactions and pathogenesis; viral immunology and vaccines; emerging and re-emerging viral diseases; antiviral therapy; innovative diagnostics; viral biotechnologies and gene therapy. As in the previous edition (Salata and Palù, 2004 J Cell Physiol 199:171-173), a specific topic was thoroughly covered in a roundtable. In this edition the overviewed topic was HCV, from epidemiology and genetic variability to immunology and antiviral therapy. The final program can be found at the web site http://www.siv-virologia.it. A summary of the oral presentations of the 2004 meeting is reported.