286 research outputs found
Viral and Bacterial Pathogens in Bovine Respiratory Disease in Finland
Pathogens causing bovine respiratory tract disease in Finland were investigated. Eighteen cattle herds with bovine respiratory disease were included. Five diseased calves from each farm were chosen for closer examination and tracheobronchial lavage. Blood samples were taken from the calves at the time of the investigation and from 86 calves 3â4 weeks later. In addition, 6â10 blood samples from animals of different ages were collected from each herd, resulting in 169 samples. Serum samples were tested for antibodies to bovine parainfluenza virus-3 (PIV-3), bovine respiratory syncytial virus (BRSV), bovine coronavirus (BCV), bovine adenovirus-3 (BAV-3) and bovine adenovirus-7 (BAV-7). About one third of the samples were also tested for antibodies to bovine virus diarrhoea virus (BVDV) with negative results. Bacteria were cultured from lavage fluid and in vitro susceptibility to selected antimicrobials was tested. According to serological findings, PIV-3, BAV-7, BAV-3, BCV and BRSV are common pathogens in Finnish cattle with respiratory problems. A titre rise especially for BAV-7 and BAV-3, the dual growth of Mycoplasma dispar and Pasteurella multocida, were typical findings in diseased calves. Pasteurella sp. strains showed no resistance to tested antimicrobials. Mycoplasma bovis and Mannheimia haemolytica were not found
The International Trade Network: weighted network analysis and modelling
Tools of the theory of critical phenomena, namely the scaling analysis and
universality, are argued to be applicable to large complex web-like network
structures. Using a detailed analysis of the real data of the International
Trade Network we argue that the scaled link weight distribution has an
approximate log-normal distribution which remains robust over a period of 53
years. Another universal feature is observed in the power-law growth of the
trade strength with gross domestic product, the exponent being similar for all
countries. Using the 'rich-club' coefficient measure of the weighted networks
it has been shown that the size of the rich-club controlling half of the
world's trade is actually shrinking. While the gravity law is known to describe
well the social interactions in the static networks of population migration,
international trade, etc, here for the first time we studied a non-conservative
dynamical model based on the gravity law which excellently reproduced many
empirical features of the ITN.Comment: 5 pages, 5 figure
Network centrality: an introduction
Centrality is a key property of complex networks that influences the behavior
of dynamical processes, like synchronization and epidemic spreading, and can
bring important information about the organization of complex systems, like our
brain and society. There are many metrics to quantify the node centrality in
networks. Here, we review the main centrality measures and discuss their main
features and limitations. The influence of network centrality on epidemic
spreading and synchronization is also pointed out in this chapter. Moreover, we
present the application of centrality measures to understand the function of
complex systems, including biological and cortical networks. Finally, we
discuss some perspectives and challenges to generalize centrality measures for
multilayer and temporal networks.Comment: Book Chapter in "From nonlinear dynamics to complex systems: A
Mathematical modeling approach" by Springe
Message-Passing Methods for Complex Contagions
Message-passing methods provide a powerful approach for calculating the
expected size of cascades either on random networks (e.g., drawn from a
configuration-model ensemble or its generalizations) asymptotically as the
number of nodes becomes infinite or on specific finite-size networks. We
review the message-passing approach and show how to derive it for
configuration-model networks using the methods of (Dhar et al., 1997) and
(Gleeson, 2008). Using this approach, we explain for such networks how to
determine an analytical expression for a "cascade condition", which determines
whether a global cascade will occur. We extend this approach to the
message-passing methods for specific finite-size networks (Shrestha and Moore,
2014; Lokhov et al., 2015), and we derive a generalized cascade condition.
Throughout this chapter, we illustrate these ideas using the Watts threshold
model.Comment: 14 pages, 3 figure
Characterizing the community structure of complex networks
Community structure is one of the key properties of complex networks and
plays a crucial role in their topology and function. While an impressive amount
of work has been done on the issue of community detection, very little
attention has been so far devoted to the investigation of communities in real
networks. We present a systematic empirical analysis of the statistical
properties of communities in large information, communication, technological,
biological, and social networks. We find that the mesoscopic organization of
networks of the same category is remarkably similar. This is reflected in
several characteristics of community structure, which can be used as
``fingerprints'' of specific network categories. While community size
distributions are always broad, certain categories of networks consist mainly
of tree-like communities, while others have denser modules. Average path
lengths within communities initially grow logarithmically with community size,
but the growth saturates or slows down for communities larger than a
characteristic size. This behaviour is related to the presence of hubs within
communities, whose roles differ across categories. Also the community
embeddedness of nodes, measured in terms of the fraction of links within their
communities, has a characteristic distribution for each category. Our findings
are verified by the use of two fundamentally different community detection
methods.Comment: 15 pages, 20 figures, 4 table
Hidden geometric correlations in real multiplex networks
Real networks often form interacting parts of larger and more complex
systems. Examples can be found in different domains, ranging from the Internet
to structural and functional brain networks. Here, we show that these multiplex
systems are not random combinations of single network layers. Instead, they are
organized in specific ways dictated by hidden geometric correlations between
the individual layers. We find that these correlations are strong in different
real multiplexes, and form a key framework for answering many important
questions. Specifically, we show that these geometric correlations facilitate:
(i) the definition and detection of multidimensional communities, which are
sets of nodes that are simultaneously similar in multiple layers; (ii) accurate
trans-layer link prediction, where connections in one layer can be predicted by
observing the hidden geometric space of another layer; and (iii) efficient
targeted navigation in the multilayer system using only local knowledge, which
outperforms navigation in the single layers only if the geometric correlations
are sufficiently strong. Our findings uncover fundamental organizing principles
behind real multiplexes and can have important applications in diverse domains.Comment: Supplementary Materials available at
http://www.nature.com/nphys/journal/v12/n11/extref/nphys3812-s1.pd
The physics of spreading processes in multilayer networks
The study of networks plays a crucial role in investigating the structure,
dynamics, and function of a wide variety of complex systems in myriad
disciplines. Despite the success of traditional network analysis, standard
networks provide a limited representation of complex systems, which often
include different types of relationships (i.e., "multiplexity") among their
constituent components and/or multiple interacting subsystems. Such structural
complexity has a significant effect on both dynamics and function. Throwing
away or aggregating available structural information can generate misleading
results and be a major obstacle towards attempts to understand complex systems.
The recent "multilayer" approach for modeling networked systems explicitly
allows the incorporation of multiplexity and other features of realistic
systems. On one hand, it allows one to couple different structural
relationships by encoding them in a convenient mathematical object. On the
other hand, it also allows one to couple different dynamical processes on top
of such interconnected structures. The resulting framework plays a crucial role
in helping achieve a thorough, accurate understanding of complex systems. The
study of multilayer networks has also revealed new physical phenomena that
remain hidden when using ordinary graphs, the traditional network
representation. Here we survey progress towards attaining a deeper
understanding of spreading processes on multilayer networks, and we highlight
some of the physical phenomena related to spreading processes that emerge from
multilayer structure.Comment: 25 pages, 4 figure
Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks : an international rare cancers initiative (IRCI) ocular melanoma study
Background: Despite the completion of numerous phase II studies, a standard of care treatment has yet to be defined for metastatic uveal melanoma (mUM). To determine benchmarks of progression free survival (PFS) and overall survival (OS), we carried out a meta-analysis using individual patient level trial data. Methods: Individual patient variables and survival outcomes were requested from 29 trials published from 2000 to 2016. Univariable and multivariable analysis were carried out for prognostic factors. The variability between trial arms and between therapeutic agents on PFS and OS was investigated. Results: OS data were available for 912 patients. The median PFS was 3.3 months (95% CI 2.9-3.6) and 6-month PFS rate was 27% (95% CI 24-30). Univariable analysis showed male sex, elevated (i.e. > versus = 3 cm versus Conclusion: Benchmarks of 6-month PFS and 1-year OS rates were determined accounting for prognostic factors. These may be used to facilitate future trial design and stratification in mUM.Peer reviewe
Contexts and Approaches to Multiprofessional Working in Arts and Social Care
In this article, we identify the basic concepts informing multiprofessional competencies in arts and social work/care, focusing on their specific cultural contextualisation, as framed within the currently running project MOMU (Moving towards Multiprofessional Work in Art and Social Work) funded by the Erasmus+ Programme.[1] In short, the project aims to define competencies in teamwork and enhance educational/teacher knowledge and skills in arts and social work/care (MPW) by developing learning materials and handbooks in this area and embedding this in undergraduate HE provision. It builds on the work carried out in the project MIMO â Moving In, Moving On! which established and embedded the initial methods for MPW into professional practice in Finland and Estonia[2]. (TUAS, 2013) The emphasis of this kind of MPW work lies in combining the strengths of different arts and social work/care professionals to work effectively together with individuals or communities to address the identified needs. It is a multiprofessional practice stemming from a multidisciplinary approach to working with communities and individuals. This article will thus aim to a) articulate the cultural and critical contexts of relevant concepts and b) propose overarching criteria for learning frameworks which inform future training modules in the area of MPW
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