Dynamics and Function of Langerhans Cells In Vivo: Dermal Dendritic Cells Colonize Lymph Node Areas Distinct from Slower Migrating Langerhans Cells

Langerhans cells (LCs) are prominent dendritic cells (DCs) in epithelia, but their role in immunity is poorly defined. To track and discriminate LCs from dermal DCs in vivo, we developed knockin mice expressing enhanced green fluorescent protein (EGFP) under the control of the langerin (CD207) gene. By using vital imaging, we showed that most EGFP(+) LCs were sessile under steady-state conditions, whereas skin inflammation induced LC motility and emigration to lymph nodes (LNs). After skin immunization, dermal DCs arrived in LNs first and colonized areas distinct from slower migrating LCs. LCs reaching LNs under steady-state or inflammatory conditions expressed similar levels of costimulatory molecules. Langerin and EGFP were also expressed on thymic DCs and on blood-derived, CD8alpha(+) DCs from all secondary lymphoid organs. By using a similar knockin strategy involving a diphtheria toxin receptor (DTR) fused to EGFP, we demonstrated that LCs were dispensable for triggering hapten-specific T cell effectors through skin immunization.

Parameter sensitivity for optimal design of 65 nm node SOI transistors

Mixed-mode simulation, where device simulation is embedded directly within a circuit simulator, is used for the first time to provide scaling guidelines to achieve optimal digital circuit performance for double gate SOI MOSFETs. This significant advance overcomes the lack of availability of SPICE model parameters. The sensitivity of the gate delay and on-off current ratio to each of the key geometric and technological parameters of the transistor is quantified. The impact of the source-drain doping profile on circuit performance is comprehensively investigated.

Fixed-node diffusion Monte Carlo computations for closed-shell jellium spheres

Fixed-node diffusion Monte Carlo computations are used to determine the ground state energy and electron density for jellium spheres with up to N = 106 electrons and background densities corresponding to the electron gas parameter 1 less than or equal to r(s)less than or equal to5.62. We analyze the density and size dependence of the surface energy, and we extrapolate our data to the thermodynamic limit. The results agree well with the predictions of density functional computations using the local density approximation. In the case of N = 20, we extend our computation to higher densities and identify a transition between atomic- and jelliumlike nodal structures occurring at the background density corresponding to r(s)=0.13. In this case the local density approximation is unable to reproduce the changes in the correlation energy due to the discontinuous transition in the ground state nodal structure. We discuss the relevance of our results for nonlocal approximations to density functional theory.

Cutting edge: Langerin+ dendritic cells in the mesenteric lymph node set the stage for skin and gut immune system cross-talk.

Topical transcutaneous immunization (TCI) presents many clinical advantages, but its underlying mechanism remains unknown. TCI induced Ag-specific IgA Ab-secreting cells expressing CCR9 and CCR10 in the small intestine in a retinoic acid-dependent manner. These intestinal IgA Abs were maintained in Peyer\'s patch-null mice but abolished in the Peyer\'s patch- and lymph node-null mice. The mesenteric lymph node (MLN) was shown to be the site of IgA isotype class switching after TCI. Unexpectedly, langerin(+)CD8alpha(-) dendritic cells emerged in the MLN after TCI; they did not migrate from the skin but rather differentiated rapidly from bone marrow precursors. Depletion of langerin(+) cells impaired intestinal IgA Ab responses after TCI. Taken together, these findings suggest that MLN is indispensable for the induction of intestinal IgA Abs following skin immunization and that cross-talk between the skin and gut immune systems might be mediated by langerin(+) dendritic cells in the MLN.

Effect of Selfish Node Behavior on Efficient Topology Design

The problem of topology control is to assign per-node transmission power such that the resulting topology is energy efficient and satisfies certain global properties such as connectivity. The conventional approach to achieve these objectives is based on the fundamental assumption that nodes are socially responsible. We examine the following question: if nodes behave in a selfish manner, how does it impact the overall connectivity and energy consumption in the resulting topologies? We pose the above problem as a noncooperative game and use game-theoretic analysis to address it. We study Nash equilibrium properties of the topology control game and evaluate the efficiency of the induced topology when nodes employ a greedy best response algorithm. We show that even when the nodes have complete information about the network, the steady-state topologies are suboptimal. We propose a modified algorithm based on a better response dynamic and show that this algorithm is guaranteed to converge to energy-efficient and connected topologies. Moreover, the node transmit power levels are more evenly distributed, and the network performance is comparable to that obtained from centralized algorithms.

Improved Detection of Mycobacterium bovis Infection in Bovine Lymph Node Tissue Using Immunomagnetic Separation (IMS)-Based Methods

Immunomagnetic separation (IMS) can selectively isolate and concentrate Mycobacterium bovis cells from lymph node tissue to facilitate subsequent detection by PCR (IMS-PCR) or culture (IMS-MGIT). This study describes application of these novel IMS-based methods to test for M. bovis in a survey of 280 bovine lymph nodes (206 visibly lesioned (VL), 74 non-visibly lesioned (NVL)) collected at slaughter as part of the Northern Ireland bovine TB eradication programme. Their performance was evaluated relative to culture. Overall, 174 (62.1%) lymph node samples tested positive by culture, 162 (57.8%) by IMS-PCR (targeting IS6110), and 196 (70.0%) by IMS-MGIT culture. Twelve (6.9%) of the 174 culture positive lymph node samples were not detected by either of the IMS-based methods. However, an additional 78 M. bovis positive lymph node samples (26 (12.6%) VL and 54 (73.0%) NVL) were detected by the IMS-based methods and not by culture. When low numbers of viable M. bovis are present in lymph nodes (e.g. in NVLs of skin test reactor cattle) decontamination prior to culture may adversely affect viability, leading to false negative culture results. In contrast, IMS specifically captures whole M. bovis cells (live, dead or potentially dormant) which are not subject to any deleterious treatment before detection by PCR or MGIT culture. During this study only 2.7% of NVL lymph nodes tested culture positive, whereas 73% of the same samples tested M. bovis positive by the IMS-based tests. Results clearly demonstrate that not only are the IMS-based methods more rapid but they have greater detection sensitivity than the culture approach currently used for the detection of M. bovis infection in cattle.. Adoption of the IMS-based methods for lymph node testing would have the potential to improve M. bovis detection in clinical samples.

Application-based workload model for wireless sensor node computing platforms

Wireless sensor node platforms are very diversified and very constrained, particularly in power consumption. When choosing or sizing a platform for a given application, it is necessary to be able to evaluate in an early design stage the impact of those choices. Applied to the computing platform implemented on the sensor node, it requires a good understanding of the workload it must perform. Nevertheless, this workload is highly application-dependent. It depends on the data sampling frequency together with application-specific data processing and management. It is thus necessary to have a model that can represent the workload of applications with various needs and characteristics. In this paper, we propose a workload model for wireless sensor node computing platforms. This model is based on a synthetic application that models the different computational tasks that the computing platform will perform to process sensor data. It allows to model the workload of various different applications by tuning data sampling rate and processing. A case study is performed by modeling different applications and by showing how it can be used for workload characterization. © 2011 IEEE.