A Doppler-Cancellation Technique for Determining the Altitude Dependence of Gravitational Red Shift in an Earth Satellite

A cancellation technique permits measurement of the frequency of a source moving relative to an observer without the obscuring effect of first-order Doppler shifts. The application of this method to a gravitational red shift experiment involving the use of an earth satellite containing a highly stable oscillator is described. The rapidity with which a measurement can be made permits the taking of data at various altitudes in a given elliptical orbit. Tropospheric and ionospheric effects upon the accuracy of results are estimated

cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models

The increasing availability of sequencing data of cancer samples is fueling the development of algorithmic strategies to investigate tumor heterogeneity and infer reliable models of cancer evolution. We here build up on previous works on cancer progression inference from genomic alteration data, to deliver two distinct Cytoscape-based applications, which allow to produce, visualize and manipulate cancer evolution models, also by interacting with public genomic and proteomics databases. In particular, we here introduce cyTRON, a stand-alone Cytoscape app, and cyTRON/JS, a web application which employs the functionalities of Cytoscape/JS. cyTRON was developed in Java; the code is available at https://github.com/BIMIB-DISCo/cyTRON and on the Cytoscape App Store http://apps.cytoscape.org/apps/cytron. cyTRON/JS was developed in JavaScript and R; the source code of the tool is available at https://github.com/BIMIB-DISCo/cyTRON-js and the tool is accessible from https://bimib.disco.unimib.it/cytronjs/welcome

Ultrasonic monitoring of friction contacts during shear vibration cycles

Complex high-value jointed structures such as aero-engines are carefully designed and optimized to prevent failure and maximise their life. In the design process, physically-based numerical models are employed to predict the nonlinear dynamic response of the structure. However, the reliability of these models is limited due to the lack of accurate validation data from metallic contact interfaces subjected to high-frequency vibration cycles. In this study, ultrasonic shear waves are used to characterise metallic contact interfaces during vibration cycles, hence providing new validation data for an understanding of the state of the friction contact. Supported by numerical simulations of wave propagation within the material, a novel experimental method is developed to simultaneously acquire ultrasonic measurements and friction hysteresis loops within the same test on a high-frequency friction rig. Large variability in the ultrasound reflection/transmission is observed within each hysteresis loop and is associated with stick/slip transitions. The measurement results reveal that the ultrasound technique can be used to detect stick and slip states in contact interfaces subjected to high-frequency shear vibration. This is the first observation of this type and paves the way towards real-time monitoring of vibrating contact interfaces in jointed structures, leading to a new physical understanding of the contact states and new validation data needed for improved nonlinear dynamic analyses

A reaction-diffusion model for the growth of avascular tumor

A nutrient-limited model for avascular cancer growth including cell proliferation, motility and death is presented. The model qualitatively reproduces commonly observed morphologies for primary tumors, and the simulated patterns are characterized by its gyration radius, total number of cancer cells, and number of cells on tumor periphery. These very distinct morphological patterns follow Gompertz growth curves, but exhibit different scaling laws for their surfaces. Also, the simulated tumors incorporate a spatial structure composed of a central necrotic core, an inner rim of quiescent cells and a narrow outer shell of proliferating cells in agreement with biological data. Finally, our results indicate that the competition for nutrients among normal and cancer cells may be a determinant factor in generating papillary tumor morphology.Comment: 9 pages, 6 figures, to appear in PR

World citation and collaboration networks: uncovering the role of geography in science

Modern information and communication technologies, especially the Internet, have diminished the role of spatial distances and territorial boundaries on the access and transmissibility of information. This has enabled scientists for closer collaboration and internationalization. Nevertheless, geography remains an important factor affecting the dynamics of science. Here we present a systematic analysis of citation and collaboration networks between cities and countries, by assigning papers to the geographic locations of their authors' affiliations. The citation flows as well as the collaboration strengths between cities decrease with the distance between them and follow gravity laws. In addition, the total research impact of a country grows linearly with the amount of national funding for research & development. However, the average impact reveals a peculiar threshold effect: the scientific output of a country may reach an impact larger than the world average only if the country invests more than about 100,000 USD per researcher annually.Comment: Published version. 9 pages, 5 figures + Appendix, The world citation and collaboration networks at both city and country level are available at http://becs.aalto.fi/~rajkp/datasets.htm

Patient-Powered Research Networks of the Autoimmune Research Collaborative: Rationale, Capacity, and Future Directions

Patient-Powered Research Networks (PPRNs) are US-based registry infrastructures co-created by advocacy groups, patient research partners, academic investigators, and other healthcare stakeholders. Patient-Powered Research Networks collect information directly from patients to conduct and disseminate the results of patient-centered/powered research that helps patients make more informed decisions about their healthcare. Patient-Powered Research Networks gather and utilize real-world data and patient-reported outcomes to conduct comparative effectiveness, safety, and other research, and leverage the Internet to accomplish this effectively and efficiently. Four PPRNs focused on autoimmune and immune-mediated conditions formed the Autoimmune Research Collaborative: ArthritisPower (rheumatoid arthritis, spondyloarthritis, and other rheumatic and musculoskeletal diseases), IBD Partners (inflammatory bowel disease), iConquerMS (multiple sclerosis), and the Vasculitis PPRN (vasculitis). The Autoimmune Research Collaborative aims to inform the healthcare decision making of patients, care partners, and other stakeholders, such as clinicians, regulators, and payers. Illustrated by practical applications from the Autoimmune Research Collaborative and its constituent PPRNs, this article discusses the shared capacities and challenges of the PPRN model, and the opportunities presented by collaborating across autoimmune conditions to design, conduct, and disseminate patient-centered outcomes research

A Game Theoretic Model for the Formation of Navigable Small-World Networks

Kleinberg proposed a family of small-world networks to ex-plain the navigability of large-scale real-world social net-works. However, the underlying mechanism that drives real networks to be navigable is not yet well understood. In this paper, we present a game theoretic model for the for-mation of navigable small world networks. We model the network formation as a game in which people seek for both high reciprocity and long-distance relationships. We show that the navigable small-world network is a Nash Equilib-rium of the game. Moreover, we prove that the navigable small-world equilibrium tolerates collusions of any size and arbitrary deviations of a large random set of nodes, while non-navigable equilibria do not tolerate small group collu-sions or random perturbations. Our empirical evaluation further demonstrates that the system always converges to the navigable network even when limited or no information about other players ’ strategies is available. Our theoretical and empirical analyses provide important new insight on the connection between distance, reciprocity and navigability in social networks

Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure

Relationships between CYP2D6 phenotype, breast cancer and hot flushes in women at high risk of breast cancer receiving prophylactic tamoxifen: results from the IBIS-I trial

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