Association between tuberculosis in men and social network structure in Kampala, Uganda

Background Globally, tuberculosis disease (TB) is more common among males than females. Recent research proposes that differences in social mixing by sex could alter infection patterns in TB. We examine evidence for two mechanisms by which social-mixing could increase men’s contact rates with TB cases. First, men could be positioned in social networks such that they contact more people or social groups. Second, preferential mixing by sex could prime men to have more exposure to TB cases. Methods We compared the networks of male and female TB cases and healthy matched controls living in Kampala, Uganda. Specifically, we estimated their positions in social networks (network distance to TB cases, degree, betweenness, and closeness) and assortativity patterns (mixing with adult men, women, and children inside and outside the household). Results The observed network consisted of 11,840 individuals. There were few differences in estimates of node position by sex. We found distinct mixing patterns by sex and TB disease status including that TB cases have proportionally more adult male contacts and fewer contacts with children. Conclusions This analysis used a network approach to study how social mixing patterns are associated with TB disease. Understanding these mechanisms may have implications for designing targeted intervention strategies in high-burden populations.

334 Localisation of Sentinel Node for Cutaneous Melanoma of Trunk

Aim Our aim was to review localisation of sentinel node for cutaneous melanoma of trunk based on position. This was to investigate whether we can predict sentinel node position and therefore avoid scintigram. Method We have collected 50 patients with cutaneous melanoma of the trunk who underwent sentinel node biopsy. We have reviewed and recorded position of the melanoma and most likely position of sentinel biopsy. This has been on the day of surgery compared to scintigram. Results Majority of the melanomas have drained into the predicted location. Midline melanomas had tendency to drain bilaterally. The most difficult to predict were melanomas on the anterior aspect of trunk. Conclusions In majority of cases surgeons were able to predict position of sentinel node biopsy and therefore scintigram might not be necessary. If such approach is adapted, the need for radiologist involvement on the day of surgery may decrease.

A Direct Comparison of Node and Element-based Finite Element Modeling Approaches to Study Tissue Growth

Finite element analysis is a useful tool to model growth of biological tissues and predict how growth can be impacted by stimuli. Previous work has simulated growth using node-based or element-based approaches, and this implementation choice may influence predicted growth, irrespective of the applied growth model. This study directly compared node-based and element-based approaches to understand the isolated impact of implementation method on growth predictions by simulating growth of a bone rudiment geometry, and determined what conditions produce similar results between the approaches. We used a previously reported node-based approach implemented via thermal expansion and an element-based approach implemented via osmotic swelling, and we derived a mathematical relationship to relate the growth resulting from these approaches. We found that material properties (modulus) affected growth in the element-based approach, with growth completely restricted for high modulus values relative to the growth stimulus, and no restriction for low modulus values. The node-based approach was unaffected by modulus. Node- and element- based approaches matched marginally better when the conversion coefficient to relate the approaches was optimized based on results of initial simulations, rather than using the theoretically-predicted conversion coefficient (median difference in node position 0.042 cm vs. 0.052 cm, respectively). In summary, we illustrate here the importance of the choice of implementation approach for modeling growth, provide a framework for converting models between implementation approaches, and highlight important considerations for comparing results in prior work and developing new models of tissue growth.

Clusters of neuronal Neurofascin prefigure node of Ranvier position along single axons

The spacing of nodes of Ranvier crucially affects conduction properties along myelinated axons. It has been assumed that node position is primarily driven by the growth of myelin sheaths. Here, we reveal an additional mechanism of node positioning that is driven by the axon. We show through longitudinal live imaging of node formation dynamics that stable clusters of the cell adhesion molecule Neurofascin A accumulate at specific sites along axons prior to myelination. While some of these clusters change position upon encounter with growing myelin sheaths, others restrict sheath extension and are therefore predictive of future node position. Animals that lack full-length Neurofascin A showed increased internodal distances and less regular spacing of nodes along single axons. Together, our data reveal the existence of an axonal mechanism to position its nodes of Ranvier that does not depend on regulation of myelin sheath length.

A method for simultaneous minimal-cost supply node location and network design in pipelined infrastructure.

<p>Designing low-cost infrastructure networks for transport of hydrogen represents a key step in the adoption and penetration of hydrogen technology in a low-carbon energy future.</p><p>For hydrogen distribution, network design amounts to creating pipeline systems in which supply is matched to demand through a transportation system that respects multiple constraints (technical, social, environmental) and minimizes cost. This can equate to recycling pre-existing pipelines or building new ones, but also involves the placement of carefully chosen supply nodes.</p><p>In a multi-level distribution network, supply nodes may assume many roles from large-capacity geological storage facilities, to local relay nodes addressing the end customers.</p><p>Finding minimum-cost pipeline network designs in which supply node locations are already chosen is itself a well-studied combinatorial optimization problem (Cayley’s formula predicts  possible spanning trees for  nodes) for which multiple heuristic and exact methods are known [1].</p><p>Allowing the supply node to take any position within the network renders the problem significantly more complex as the minimum-cost network topology (the specific connections to between nodes) will potentially change for each new supply node position.</p><p>We propose a heuristic algorithm that finds good solutions in a reasonable amount of time based on a back-and-forth between:</p><p>- Repositioning optimally the supply node, while maintaining the same connections to the supply node (reduces cost)</p><p>- Optimizing the network topology, assuming a fixed supply node position (also reduces cost)</p><p>The algorithm stops once no further cost reductions for the network design are found. The algorithm output is found to be sensitive to the initial guess of the supply node position, the initial guess of the connections to the supply node, and to the specific “path” of the back-and-forth taken to reach the given local minimum. As such, a good initial guess for a “housing polygon”, i.e. the nodes to which supply node is directly connected to, is crucial in finding the minimum-cost solution, and in the shortest time possible. We attempt to make this initial guess with a machine learning algorithm, with features describing the geometrical distribution of node capacity, as well as elementary network concepts.</p><p>Finally, an example is provided on a model hydrogen network comprised of typical elements and realistic cost-functions.</p><p> </p><p>[1]: Brimberg J, Hansen P, Lin K, Mladenovi N, Breton M, Brimberg, J (2003) An oil pipeline design problem. Operations Research, 51(2):228–239. https://doi.org/10.1287/opre.51.2.228.12786</p>

Effects of Node Position and Electric Conductivity of Nutrient Solution on Adventitious Rooting of Nasturtium (Tropaeolum majus L.) Cuttings

Nasturtium is a popular herbal plant, widely cultivated as culinary and medicinal plants all over the world. However, the seed propagation of nasturtium is inefficient, and in-vitro propagation is sophisticated and high-cost. In this study, the cutting propagation method was employed to produce nasturtium seedlings. We aimed to determine the optimal conditions for cutting propagation of nasturtium seedlings by investigating the effects of node position and electric conductivity (EC) of nutrient solution on the root formation of the cuttings. Cuttings from five node positions (apical bud, 2nd node, 3rd node, 4th node, and 5th node) were subjected to water and five EC (1.0, 2.0, 3.0, 4.0, and 5.0 dS m−1) treatments with a hydroponic cultivation system in a plant factory. Results showed that all cuttings rooted successfully within two weeks. The cuttings from the apical bud position rooted earliest and produced the most roots regardless of EC level. Cuttings from other node positions produced longer roots and heavier root fresh and dry weights than those from the apical bud position. The cuttings under EC of 1.0 dS m−1 had the greatest root number, the longest root length, and the heaviest root fresh and dry weights regardless of node positions. The EC of 1.0 dS m−1 is considered the best condition for nasturtium cuttings for the range of EC tested in this study, and the cuttings from all the five node positions can be used as seedling materials.

Online Sequential DV-Hop Localization Algorithm for Wireless Sensor Networks

One of the main issues of wireless sensor networks is localization. Besides, it is important to track and analyze the sensed information. The technique of localization can calculate node position with the help of a set of designed nodes, denoted as anchors. The set density of these anchors may be incremented or decremented because of many reasons such as maintenance, lifetime, and breakdown. The well-known Distance Vector Hop (DV-Hop) algorithm is a suitable solution for localizing nodes having few neighbor anchors. However, existing DV-Hop-based localization methods have not considered the problem of anchor breakdown which may happen during the localization process. In order to avoid this issue, an Online Sequential DV-Hop algorithm is proposed in this paper to sequentially calculate positions of nodes and improve accuracy of node localization for multihop wireless sensor networks. The algorithm deals with the variation of the number of available anchors in the network. We note that DV-Hop algorithm is used in this article to process localization of nodes by a new optimized method for the estimation of the average distance of hops between nodes. Our proposed localization method is based on an online sequential computation. Compared with the original DV-Hop and other localization methods from the literature, simulation results prove that the proposed algorithm greatly minimizes the average of localization error of sensor nodes.

Design and implementation of grid based clustering in WSN using dynamic sink node

A wireless sensor networks (WSNs) play a significant application, especially in the monitored remoting environmental, which enables by the availability of sensors which are cheaper, smaller, and intelligent. The equipment of such sensors be with wireless interfaces, which a communication with other sensors occurs for creating a network, that contains many distributed nodes. The closest nodes to the sink are exploited at an enormous traffic load while the data from the whole regions are forwarded between them to reach the sink. This result in exhausting their energy quickly and partitioning the network. This is solved by changing the sink node position in Grid based clustering technique, which considers the optimal method for this purpose. A simulation with MATLAB can be applied for grid based clustering technique to evaluate the performance of WSN. The expected results deal with outperforms in throughput, reducing energy consumption and increasing residual energy, in addition to prolong the network lifetime of the sensor network