3,148 research outputs found
Bioassays for coastal water quality: an assessment using the larval development of Haliotis midae L
The United States Environmental Protection Agency (USEPA) has established a suite of methods that use coastal invertebrate species as bioassay organisms to test industrial and domestic effluent as well as coastal waters for potential toxicity. Although these methods are used globally, the potential of such toxicity tests has not been adequately explored for South African coastal waters. This study serves to describe a simple, cost-effective and relatively quick testing procedure using the development of Haliotis midae larvae as a bioassay of coastal water quality. This test is based on the sensitivity of these larvae to low concentrations of zinc (Zn). Its performance in a field trial demonstrates not only that this test has the potential to identify coastal waters of poor quality, but also that such identification could be of value in attempts to restock natural abalone populations, which are under extreme pressure from legal and illegal exploitation. Further work in this line should focus on the refinement of the methodology for this and other local species and should aim to contribute to the development of suitable criteria for the management of coastal water quality in South Africa.
WaterSA Vol.28(4) 2002: 457-46
Network 'small-world-ness': a quantitative method for determining canonical network equivalence
Background: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction ('small/not-small') rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model-the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified.
Methodology/Principal Findings: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S. 1-an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process.
Conclusions/Significance: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing
Improving the quality of cognitive screening assessments: ACEmobile, an iPad-based version of the Addenbrooke's Cognitive Examination-III.
INTRODUCTION: Ensuring reliable administration and reporting of cognitive screening tests are fundamental in establishing good clinical practice and research. This study captured the rate and type of errors in clinical practice, using the Addenbrooke's Cognitive Examination-III (ACE-III), and then the reduction in error rate using a computerized alternative, the ACEmobile app. METHODS: In study 1, we evaluated ACE-III assessments completed in National Health Service (NHS) clinics (n = 87) for administrator error. In study 2, ACEmobile and ACE-III were then evaluated for their ability to capture accurate measurement. RESULTS: In study 1, 78% of clinically administered ACE-IIIs were either scored incorrectly or had arithmetical errors. In study 2, error rates seen in the ACE-III were reduced by 85%-93% using ACEmobile. DISCUSSION: Error rates are ubiquitous in routine clinical use of cognitive screening tests and the ACE-III. ACEmobile provides a framework for supporting reduced administration, scoring, and arithmetical error during cognitive screening
Multiple dynamical time-scales in networks with hierarchically nested modular organization
Many natural and engineered complex networks have intricate mesoscopic
organization, e.g., the clustering of the constituent nodes into several
communities or modules. Often, such modularity is manifested at several
different hierarchical levels, where the clusters defined at one level appear
as elementary entities at the next higher level. Using a simple model of a
hierarchical modular network, we show that such a topological structure gives
rise to characteristic time-scale separation between dynamics occurring at
different levels of the hierarchy. This generalizes our earlier result for
simple modular networks, where fast intra-modular and slow inter-modular
processes were clearly distinguished. Investigating the process of
synchronization of oscillators in a hierarchical modular network, we show the
existence of as many distinct time-scales as there are hierarchical levels in
the system. This suggests a possible functional role of such mesoscopic
organization principle in natural systems, viz., in the dynamical separation of
events occurring at different spatial scales.Comment: 10 pages, 4 figure
Influence of wiring cost on the large-scale architecture of human cortical connectivity
In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (‘random’), connection length preserving (‘spatial’), and connection length optimised (‘reduced’) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain
On the complexity of color-avoiding site and bond percolation
The mathematical analysis of robustness and error-tolerance of complex
networks has been in the center of research interest. On the other hand, little
work has been done when the attack-tolerance of the vertices or edges are not
independent but certain classes of vertices or edges share a mutual
vulnerability. In this study, we consider a graph and we assign colors to the
vertices or edges, where the color-classes correspond to the shared
vulnerabilities. An important problem is to find robustly connected vertex
sets: nodes that remain connected to each other by paths providing any type of
error (i.e. erasing any vertices or edges of the given color). This is also
known as color-avoiding percolation. In this paper, we study various possible
modeling approaches of shared vulnerabilities, we analyze the computational
complexity of finding the robustly (color-avoiding) connected components. We
find that the presented approaches differ significantly regarding their
complexity.Comment: 14 page
Inference of hidden structures in complex physical systems by multi-scale clustering
We survey the application of a relatively new branch of statistical
physics--"community detection"-- to data mining. In particular, we focus on the
diagnosis of materials and automated image segmentation. Community detection
describes the quest of partitioning a complex system involving many elements
into optimally decoupled subsets or communities of such elements. We review a
multiresolution variant which is used to ascertain structures at different
spatial and temporal scales. Significant patterns are obtained by examining the
correlations between different independent solvers. Similar to other
combinatorial optimization problems in the NP complexity class, community
detection exhibits several phases. Typically, illuminating orders are revealed
by choosing parameters that lead to extremal information theory correlations.Comment: 25 pages, 16 Figures; a review of earlier work
Epidemics in partially overlapped multiplex networks
Many real networks exhibit a layered structure in which links in each layer
reflect the function of nodes on different environments. These multiple types
of links are usually represented by a multiplex network in which each layer has
a different topology. In real-world networks, however, not all nodes are
present on every layer. To generate a more realistic scenario, we use a
generalized multiplex network and assume that only a fraction of the nodes
are shared by the layers. We develop a theoretical framework for a branching
process to describe the spread of an epidemic on these partially overlapped
multiplex networks. This allows us to obtain the fraction of infected
individuals as a function of the effective probability that the disease will be
transmitted . We also theoretically determine the dependence of the epidemic
threshold on the fraction of shared nodes in a system composed of two
layers. We find that in the limit of the threshold is dominated by
the layer with the smaller isolated threshold. Although a system of two
completely isolated networks is nearly indistinguishable from a system of two
networks that share just a few nodes, we find that the presence of these few
shared nodes causes the epidemic threshold of the isolated network with the
lower propagating capacity to change discontinuously and to acquire the
threshold of the other network.Comment: 13 pages, 4 figure
Mesoscopic organization reveals the constraints governing C. elegans nervous system
One of the biggest challenges in biology is to understand how activity at the
cellular level of neurons, as a result of their mutual interactions, leads to
the observed behavior of an organism responding to a variety of environmental
stimuli. Investigating the intermediate or mesoscopic level of organization in
the nervous system is a vital step towards understanding how the integration of
micro-level dynamics results in macro-level functioning. In this paper, we have
considered the somatic nervous system of the nematode Caenorhabditis elegans,
for which the entire neuronal connectivity diagram is known. We focus on the
organization of the system into modules, i.e., neuronal groups having
relatively higher connection density compared to that of the overall network.
We show that this mesoscopic feature cannot be explained exclusively in terms
of considerations, such as optimizing for resource constraints (viz., total
wiring cost) and communication efficiency (i.e., network path length).
Comparison with other complex networks designed for efficient transport (of
signals or resources) implies that neuronal networks form a distinct class.
This suggests that the principal function of the network, viz., processing of
sensory information resulting in appropriate motor response, may be playing a
vital role in determining the connection topology. Using modular spectral
analysis, we make explicit the intimate relation between function and structure
in the nervous system. This is further brought out by identifying functionally
critical neurons purely on the basis of patterns of intra- and inter-modular
connections. Our study reveals how the design of the nervous system reflects
several constraints, including its key functional role as a processor of
information.Comment: Published version, Minor modifications, 16 pages, 9 figure
Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years
[EN] Research collaboration is necessary, rewarding, and beneficial. Cohesion between team members is related to their collective efficiency. To assess collaboration processes and their eventual outcomes, agencies need innovative methods-and social network approaches are emerging as a useful analytical tool. We identified the research output and citation data of a network of 61 research groups formally engaged in publishing rare disease research between 2000 and 2013. We drew the collaboration networks for each year and computed the global and local measures throughout the period. Although global network measures remained steady over the whole period, the local and subgroup metrics revealed a growing cohesion between the teams. Transitivity and density showed little or no variation throughout the period. In contrast the following points indicated an evolution towards greater network cohesion: the emergence of a giant component (which grew from just 30 % to reach 85 % of groups); the decreasing number of communities (following a tripling in the average number of members); the growing number of fully connected subgroups; and increasing average strength. Moreover, assortativity measures reveal that, after an initial period where subject affinity and a common geographical location played some role in favouring the connection between groups, the collaboration was driven in the final stages by other factors and complementarities. The Spanish research network on rare diseases has evolved towards a growing cohesion-as revealed by local and subgroup metrics following social network analysis.The Spanish Ministry of Economics and Competitiveness partially supported this research (Grant Number ECO2014-59381-R).Benito Amat, C.; Perruchas, F. (2016). 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