1,687 research outputs found
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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