Mixed order parameters, accidental nodes and broken time reversal symmetry in organic superconductors: a group theoretical analysis

We present a group theoretical analysis of several classes of organic superconductor. We predict that highly frustrated organic superconductors, such as K-(ET)(2)Cu-2(CN)(3) (where ET is BEDT-TTF, bis(ethylenedithio) tetrathiafulvalene) and beta'-[Pd(dmit)(2)](2)X, undergo two superconducting phase transitions, the first from the normal state to a d-wave superconductor and the second to a d + id state. We show that the monoclinic distortion of K-(ET)(2)Cu(NCS)(2) means that the symmetry of its superconducting order parameter is different from that of orthorhombic-K-(ET)(2)Cu[N(CN)(2)] Br. We propose that beta'' and theta phase organic superconductors have d(xy) + s order parameters.

Network lifetime maximization with cross-layer design in wireless sensor networks

This paper investigates a cross-layer design approach for minimizing energy consumption and maximizing network lifetime (NL) of a multiple-source and single-sink (MSSS) WSN with energy constraints. The optimization problem for MSSS WSN can be formulated as a mixed integer convex optimization problem with the adoption of time division multiple access (TDMA) in medium access control (MAC) layer, and it becomes a convex problem by relaxing the integer constraint on time slots. Impacts of data rate, link access and routing are jointly taken into account in the optimization problem formulation. Both linear and planar network topologies are considered for NL maximization (NLM). With linear MSSS and planar single-source and single-sink (SSSS) topologies, we successfully use Karush-Kuhn-Tucker (KKT) optimality conditions to derive analytical expressions of the optimal NL when all nodes are exhausted simultaneously. The problem for planar MSSS topology is more complicated, and a decomposition and combination (D&C) approach is proposed to compute suboptimal solutions. An analytical expression of the suboptimal NL is derived for a small scale planar network. To deal with larger scale planar network, an iterative algorithm is proposed for the D&C approach. Numerical results show that the upper-bounds of the network lifetime obtained by our proposed optimization models are tight. Important insights into the NL and benefits of cross-layer design for WSN NLM are obtained.

Molecular and genetic evidence for abnormalities in the nodes of Ranvier in schizophrenia

Context: Genetic, neuroimaging, and molecular neurobiological evidence support the hypothesis that the disconnectivity syndrome in schizophrenia (SZ) could arise from failures of saltatory conduction and abnormalities at the nodes of Ranvier (NOR) interface where myelin and axons interact. Objective: To identify abnormalities in the expression of oligodendroglial genes and proteins that participate in the formation, maintenance, and integrity of the NOR in SZ. Design: The messenger RNA (mRNA) expression levels of multiple NOR genes were quantified in 2 independent postmortem brain cohorts of individuals with SZ, and generalizability to protein expression was confirmed. The effect of the ANK3 genotype on the mRNA expression level was tested in postmortem human brain. Case-control analysis tested the association of the ANK3 genotype with SZ. The ANK3 genotype's influence on cognitive task performance and functional magnetic resonance imaging activation was tested in 2 independent cohorts of healthy individuals. Setting: Research hospital. Patients: Postmortem samples from patients with SZ and healthy controls were used for the brain expression study (n=46) and the case-control analysis (n=272). Healthy white men and women participated in the cognitive (n=513) and neuroimaging (n=52) studies. Main Outcome Measures: The mRNA and protein levels in postmortem brain samples, genetic association with schizophrenia, cognitive performance, and blood oxygenation level-dependent functional magnetic resonance imaging. Results: The mRNA expression of multiple NOR genes was decreased in schizophrenia. The ANK3 rs9804190 C allele was associated with lower ANK3 mRNA expression levels, higher risk for SZ in the case-control cohort, and poorer working memory and executive function performance and increased prefrontal activation during a working memory task in healthy individuals. Conclusions: These results point to abnormalities in the expression of genes and protein associated with the integrity of the NOR and suggest them as substrates for the disconnectivity syndrome in SZ. The association of ANK3 with lower brain mRNA expression levels implicates a molecular mechanism for its genetic, clinical, and cognitive associations with SZ. ©2012 American Medical Association. All rights reserved.

Radio range adjustment for energy efficient wireless sensor networks

In wireless ad hoc sensor networks, energy use is in many cases the most important constraint since it corresponds directly to operational lifetime. Topology management schemes such as GAF put the redundant nodes for routing to sleep in order to save the energy. The radio range will affect the number of neighbouring nodes, which collaborate to forward data to a base station or sink. In this paper we study a simple linear network and deduce the relationship between optimal radio range and traffic. We find that half of the power can be saved if the radio range is adjusted appropriately compared with the best case where equal radio ranges are used.

Determining the number of nodes for wireless sensor networks

The number of nodes has large impact on the performance, lifetime and cost of wireless sensor network (WSN). It is difficult to determine, because it depends on many factors, such as the network protocols, the collaborative signal processing (CSP) algorithms, etc. A mathematical model is proposed in this paper to calculate the number based on the required working time. It can be used in the general situation by treating these factors as the parameters of energy consumption. © 2004 IEEE.

Seagrass sediments as a global carbon sink: Isotopic constraints

Seagrass meadows are highly productive habitats found along many of the world's coastline, providing important services that support the overall functioning of the coastal zone. The organic carbon that accumulates in seagrass meadows is derived not only from seagrass production but from the trapping of other particles, as the seagrass canopies facilitate sedimentation and reduce resuspension. Here we provide a comprehensive synthesis of the available data to obtain a better understanding of the relative contribution of seagrass and other possible sources of organic matter that accumulate in the sediments of seagrass meadows. The data set includes 219 paired analyses of the carbon isotopic composition of seagrass leaves and sediments from 207 seagrass sites at 88 locations worldwide. Using a three source mixing model and literature values for putative sources, we calculate that the average proportional contribution of seagrass to the surface sediment organic carbon pool is ∼50%. When using the best available estimates of carbon burial rates in seagrass meadows, our data indicate that between 41 and 66 gC m−2 yr−1 originates from seagrass production. Using our global average for allochthonous carbon trapped in seagrass sediments together with a recent estimate of global average net community production, we estimate that carbon burial in seagrass meadows is between 48 and 112 Tg yr−1, showing that seagrass meadows are natural hot spots for carbon sequestration.

The seasonal sea-ice zone in the glacial Southern Ocean as a carbon sink, supplementary datasets

Reduced surface-deep ocean exchange and enhanced nutrient consumption by phytoplankton in the Southern Ocean have been linked to lower glacial atmospheric CO2. However, identification of the biological and physical conditions involved and the related processes remains incomplete. Here we specify Southern Ocean surface-subsurface contrasts using a new tool, the combined oxygen and silicon isotope measurement of diatom and radiolarian opal, in combination with numerical simulations. Our data do not indicate a permanent glacial halocline related to melt water from icebergs. Corroborated by numerical simulations, we find that glacial surface stratification was variable and linked to seasonal sea-ice changes. During glacial spring-summer, the mixed layer was relatively shallow, while deeper mixing occurred during fall-winter, allowing for surface-ocean refueling with nutrients from the deep reservoir, which was potentially richer in nutrients than today. This generated specific carbon and opal export regimes turning the glacial seasonal sea-ice zone into a carbon sink.

Diagnostic utility of thyroglobulin measurement in the fine needle aspirates from cervical lymph nodes: a case report

Fine needle aspiration cytology (FNAC) is the more accurate diagnostic method for cervical lymph node (CLN) metastasis from differentiated thyroid cancers (DTC). However, FNAC diagnosis of cystic CLN is, in most cases, uninformative due to inadequate cellularity. Recently, thyroglobulin (Tg) detection in FNAC needle washout fluid has been shown to improve the diagnostic accuracy of FNAC, and its routine association with cytology is recommended. We here describe the case of a 20 yr old girl complaining of the recent appearance of palpable non-painful laterocervical nodes in the neck. Ultrasound examination revealed the presence of 3 cystic CLNs and 2 mixed thyroid nodules, with the larger one showing irregular margins. On the latter, and on 2 larger CLNs, FNAC was performed, and both Tg protein and mRNA were determined in the needle washout. The cytological analysis was not diagnostic for the two CLNs, while that of the thyroid nodule reported the presence of colloid and groups of thyrocytes with normal morphology. Both CLNs showed, however, high levels of Tg protein and were positive for Tg mRNA, suggestive of metastatic DTC. Based on these findings, the FNAC analysis was performed on the second smaller thyroid nodule suggesting (Tir4) the presence of PTC. The patient was then subjected to total thyroidectomy with lymph nodes resection of the central and homolateral compartments. The histological diagnosis confirmed the presence of a PTC in the small nodule and metastatic lymph nodes. In conclusion, this case confirms that the cytological diagnosis of cystic lymph nodes is challenging, and that the measurement of Tg protein and/or mRNA in the needle washout may overcome this limitation.

Pathological validation and significance of micrometastasis in sentinel nodes in primary breast cancer

In embracing a multidisciplinary approach to the management of patients with sentinel node biopsy in breast cancer, the pathologist task is to screen sentinel nodes for possible metastasis. The consequences of missing sentinel node micrometastasis can directly influence treatment strategies, and this screening therefore has to be performed with more attention than usual. There is presently great diversity in the histopathological work-up of sentinel nodes, with many centres employing additional techniques such as immunohistochemistry, reverse transcription polymerase chain reaction or flow cytometry in addition to routine haematoxylin and eosin staining. In this review, we address the pathological validation and significance of micrometastasis in sentinel node biopsy in primary breast cancer

Approximation Algorithms for Network Design and Orienteering

This thesis presents approximation algorithms for some NP-Hard combinatorial optimization problems on graphs and networks; in particular, we study problems related to Network Design. Under the widely-believed complexity-theoretic assumption that P is not equal to NP, there are no efficient (i.e., polynomial-time) algorithms that solve these problems exactly. Hence, if one desires efficient algorithms for such problems, it is necessary to consider approximate solutions: An approximation algorithm for an NP-Hard problem is a polynomial time algorithm which, for any instance of the problem, finds a solution whose value is guaranteed to be within a multiplicative factor of the value of an optimal solution to that instance. We attempt to design algorithms for which this factor, referred to as the approximation ratio of the algorithm, is as small as possible. The field of Network Design comprises a large class of problems that deal with constructing networks of low cost and/or high capacity, routing data through existing networks, and many related issues. In this thesis, we focus chiefly on designing fault-tolerant networks. Two vertices u,v in a network are said to be k-edge-connected if deleting any set of k − 1 edges leaves u and v connected; similarly, they are k-vertex connected if deleting any set of k − 1 other vertices or edges leaves u and v connected. We focus on building networks that are highly connected, meaning that even if a small number of edges and nodes fail, the remaining nodes will still be able to communicate. A brief description of some of our results is given below. We study the problem of building 2-vertex-connected networks that are large and have low cost. Given an n-node graph with costs on its edges and any integer k, we give an O(log n log k) approximation for the problem of finding a minimum-cost 2-vertex-connected subgraph containing at least k nodes. We also give an algorithm of similar approximation ratio for maximizing the number of nodes in a 2-vertex-connected subgraph subject to a budget constraint on the total cost of its edges. Our algorithms are based on a pruning process that, given a 2-vertex-connected graph, finds a 2-vertex-connected subgraph of any desired size and of density comparable to the input graph, where the density of a graph is the ratio of its cost to the number of vertices it contains. This pruning algorithm is simple and efficient, and is likely to find additional applications. Recent breakthroughs on vertex-connectivity have made use of algorithms for element-connectivity problems. We develop an algorithm that, given a graph with some vertices marked as terminals, significantly simplifies the graph while preserving the pairwise element-connectivity of all terminals; in fact, the resulting graph is bipartite. We believe that our simplification/reduction algorithm will be a useful tool in many settings. We illustrate its applicability by giving algorithms to find many trees that each span a given terminal set, while being disjoint on edges and non-terminal vertices; such problems have applications in VLSI design and other areas. We also use this reduction algorithm to analyze simple algorithms for single-sink network design problems with high vertex-connectivity requirements; we give an O(k log n)-approximation for the problem of k-connecting a given set of terminals to a common sink. We study similar problems in which different types of links, of varying capacities and costs, can be used to connect nodes; assuming there are economies of scale, we give algorithms to construct low-cost networks with sufficient capacity or bandwidth to simultaneously support flow from each terminal to the common sink along many vertex-disjoint paths. We further investigate capacitated network design, where edges may have arbitrary costs and capacities. Given a connectivity requirement R_uv for each pair of vertices u,v, the goal is to find a low-cost network which, for each uv, can support a flow of R_uv units of traffic between u and v. We study several special cases of this problem, giving both algorithmic and hardness results. In addition to Network Design, we consider certain Traveling Salesperson-like problems, where the goal is to find short walks that visit many distinct vertices. We give a (2 + epsilon)-approximation for Orienteering in undirected graphs, achieving the best known approximation ratio, and the first approximation algorithm for Orienteering in directed graphs. We also give improved algorithms for Orienteering with time windows, in which vertices must be visited between specified release times and deadlines, and other related problems. These problems are motivated by applications in the fields of vehicle routing, delivery and transportation of goods, and robot path planning.

Analysis and modelling of immune cell behaviour in lymph nodes based on multi-photon imaging

This thesis presents quantitative studies of T cell and dendritic cell (DC) behaviour in mouse lymph nodes (LNs) in the naive state and following immunisation. These processes are of importance and interest in basic immunology, and better understanding could improve both diagnostic capacity and therapeutic manipulations, potentially helping in producing more effective vaccines or developing treatments for autoimmune diseases. The problem is also interesting conceptually as it is relevant to other fields where 3D movement of objects is tracked with a discrete scanning interval. A general immunology introduction is presented in chapter 1. In chapter 2, I apply quantitative methods to multi-photon imaging data to measure how T cells and DCs are spatially arranged in LNs. This has been previously studied to describe differences between the naive and immunised state and as an indicator of the magnitude of the immune response in LNs, but previous analyses have been generally descriptive. The quantitative analysis shows that some of the previous conclusions may have been premature. In chapter 3, I use Bayesian state-space models to test some hypotheses about the mode of T cell search for DCs. A two-state mode of movement where T cells can be classified as either interacting to a DC or freely migrating is supported over a model where T cells would home in on DCs at distance through for example the action of chemokines. In chapter 4, I study whether T cell migration is linked to the geometric structure of the fibroblast reticular network (FRC). I find support for the hypothesis that the movement is constrained to the fibroblast reticular cell (FRC) network over an alternative 'random walk with persistence time' model where cells would move randomly, with a short-term persistence driven by a hypothetical T cell intrinsic 'clock'. I also present unexpected results on the FRC network geometry. Finally, a quantitative method is presented for addressing some measurement biases inherent to multi-photon imaging. In all three chapters, novel findings are made, and the methods developed have the potential for further use to address important problems in the field. In chapter 5, I present a summary and synthesis of results from chapters 3-4 and a more speculative discussion of these results and potential future directions.