Managing Uncertain Complex Events in Web of Things Applications

A critical issue in the Web of Things (WoT) is the need to process and analyze the interactions of Web-interconnected real-world objects. Complex Event Processing (CEP) is a powerful technology for analyzing streams of information about real-time distributed events, coming from different sources, and for extracting conclusions from them. However, in many situations these events are not free from uncertainty, due to either unreliable data sources and networks, measurement uncertainty, or to the inability to determine whether an event has actually happened or not. This short research paper discusses how CEP systems can incorporate different kinds of uncertainty, both in the events and in the rules. A case study is used to validate the proposal, and we discuss the benefits and limitations of this CEP extension.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

ND-Track: Tractography utilising parametric models of white matter fibre orientation dispersion

This work develops a tractography algorithm to leverage fibre dispersion estimates derived from fitting parametric models of orientation dispersion to diffusion data. Tractography techniques are powerful tools to probe white matter (WM) connectivity non-invasively. Most current techniques follow a small number of discrete directions per voxel to identify WM connections. This approach addresses the limitation of traditional DTI-based tractography for regions with crossing fibres. However, it remains an oversimplification for regions with fanning and bending configurations, where the underlying fibre orientation distributions are continuous rather than discrete [2]. Following only a discrete set of directions in this case misrepresents the underlying anatomy and is likely to result in false negative connectivity estimates. Recent parameterized models of fibre dispersion represent such sub-voxel fibre architecture more realistically and provide more accurate estimates of dispersion than non-parametric techniques such as spherical deconvolution, which are vulnerable to noise [3]. Here, we present a new tractography algorithm, hereby referred to as ND-Track (Neurite Dispersion Tracking), that leverages directional information gathered from parametric models of dispersion. We investigate the advantages of tracking with dispersion measures on a simple phantom and in in-vivo data, tracking through the coronal radiata, a region known to exhibit a significant degree of fibre dispersion. We further demonstrate that this approach does not compromise the tracking of the WM pathways for which the standard technique works well

Rapid turnover of effector-memory CD4(+) T cells in healthy humans

Memory T cells can be divided into central-memory (T(CM)) and effector-memory (T(EM)) cells, which differ in their functional properties. Although both subpopulations can persist long term, it is not known whether they are maintained by similar mechanisms. We used in vivo labeling with deuterated glucose to measure the turnover of CD4(+) T cells in healthy humans. The CD45R0(+)CCR7(-) T(EM) subpopulation was shown to have a rapid proliferation rate of 4.7% per day compared with 1.5% per day for CD45R0(+)CCR7(+) T(CM) cells; these values are equivalent to average intermitotic (doubling) times of 15 and 48 d, respectively. In contrast, the CD45RA(+)CCR7(+) naive CD4(+) T cell population was found to be much longer lived, being labeled at a rate of only 0.2% per day (corresponding to an intermitotic time of approximately 1 yr). These data indicate that human CD4(+) T(EM) cells constitute a short-lived cell population that requires continuous replenishment in vivo

Comprehensive Characterization of the Transmitted/Founder env Genes From a Single MSM Cohort in China

Background: The men having sex with men (MSM) population has become one of the major risk groups for HIV-1 infection in China. However, the epidemiological patterns, function of the env genes, and autologous and heterologous neutralization activity in the same MSM population have not been systematically characterized. Methods: The env gene sequences were obtained by the single genome amplification. The time to the most recent common ancestor was estimated for each genotype using the Bayesian Markov Chain Monte Carlo approach. Coreceptor usage was determined in NP-2 cells. Neutralization was analyzed using Env pseudoviruses in TZM-bl cells. Results: We have obtained 547 full-length env gene sequences by single genome amplification from 30 acute/early HIV-1–infected individuals in the Beijing MSM cohort. Three genotypes (subtype B, CRF01_AE, and CRF07_BC) were identified and 20% of the individuals were infected with multiple transmitted/founder (T/F) viruses. The tight clusters of the MSM sequences regardless of geographic origins indicated nearly exclusive transmission within the MSM population and limited number of introductions. The time to the most recent common ancestor for each genotype was 10–15 years after each was first introduced in China. Disparate preferences for coreceptor usages among 3 genotypes might lead to the changes in percentage of different genotypes in the MSM population over time. The genotype-matched and genotype-mismatched neutralization activity varied among the 3 genotypes. Conclusions: The identification of unique characteristics for transmission, coreceptor usage, neutralization profile, and epidemic patterns of HIV-1 is critical for the better understanding of transmission mechanisms, development of preventive strategies, and evaluation of vaccine efficacy in the MSM population in China

Beyond Crossing Fibers: Tractography Exploiting Sub-voxel Fibre Dispersion and Neighbourhood Structure

In this paper we propose a novel algorithm which leverages models of white matter fibre dispersion to improve tractography. Tractography methods exploit directional information from diffusion weighted magnetic resonance (DW-MR) imaging to infer connectivity between different brain regions. Most tractography methods use a single direction (e.g. the principal eigenvector of the diffusion tensor) or a small set of discrete directions (e.g. from the peaks of an orientation distribution function) to guide streamline propagation. This strategy ignores the effects of within-bundle orientation dispersion, which arises from fanning or bending at the sub-voxel scale, and can lead to missing connections. Various recent DW-MR imaging techniques estimate the fibre dispersion in each bundle directly and model it as a continuous distribution. Here we introduce an algorithm to exploit this information to improve tractography. The algorithm further uses a particle filter to probe local neighbourhood structure during streamline propagation. Using information gathered from neighbourhood structure enables the algorithm to resolve ambiguities between converging and diverging fanning structures, which cannot be distinguished from isolated orientation distribution functions. We demonstrate the advantages of the new approach in synthetic experiments and in vivo data. Synthetic experiments demonstrate the effectiveness of the particle filter in gathering and exploiting neighbourhood information in recovering various canonical fibre configurations and experiments with in vivo brain data demonstrate the advantages of utilising dispersion in tractography, providing benefits in practical situations. © 2013 Springer-Verlag

A spatial variation model of white matter microstructure

In this study, we introduce a new technique to model the variation of microstructural parameters across speci c brain regions. We use a simple model of di usion in each voxel, but model the variation of parameters across the region using penalised splines. We t the whole region model directly to the di usion-weighted signals. We test the tech- nique on the mid-sagittal section of the corpus callosum (CC) using a di usion MRI data set with distinct age groups. The method detects di erences and separates the groups

Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study

This paper investigates the impact of cell body (namely soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard single diffusion encoding (SDE) and more advanced double diffusion encoding (DDE) measurements using numerical simulations. The aim is to investigate the ability of dMRI/dMRS to characterize the complex morphology of brain cells focusing on these two distinctive features of brain grey matter. To this end, we employ a recently developed computational framework to create three dimensional meshes of neuron-like structures for Monte Carlo simulations, using diffusion coefficients typical of water and brain metabolites. Modelling the cellular structure as realistically connected spherical soma and cylindrical cellular projections, we cover a wide range of combinations of sphere radii and branching order of cellular projections, characteristic of various grey matter cells. We assess the impact of spherical soma size and branching order on the b-value dependence of the SDE signal as well as the time dependence of the mean diffusivity (MD) and mean kurtosis (MK). Moreover, we also assess the impact of spherical soma size and branching order on the angular modulation of DDE signal at different mixing times, together with the mixing time dependence of the apparent microscopic anisotropy (μA), a promising contrast derived from DDE measurements. The SDE results show that spherical soma size has a measurable impact on both the b-value dependence of the SDE signal and the MD and MK diffusion time dependence for both water and metabolites. On the other hand, we show that branching order has little impact on either, especially for water. In contrast, the DDE results show that spherical soma size has a measurable impact on the DDE signal's angular modulation at short mixing times and the branching order of cellular projections significantly impacts the mixing time dependence of the DDE signal's angular modulation as well as of the derived μA, for both water and metabolites. Our results confirm that SDE based techniques may be sensitive to spherical soma size, and most importantly, show for the first time that DDE measurements may be more sensitive to the dendritic tree complexity (as parametrized by the branching order of cellular projections), paving the way for new ways of characterizing grey matter morphology, non-invasively using dMRS and potentially dMRI

Linear and Nonlinear Modeling of Cerebral Flow Autoregulation Using Principal Dynamic Modes

Cerebral Flow Autoregulation (CFA) is the dynamic process by which cerebral blood flow is maintained within physiologically acceptable bounds during fluctuations of cerebral perfusion pressure. The distinction is made with “static” flow autoregulation under steady-state conditions of perfusion pressure, described by the celebrated “autoregulatory curve” with a homeostatic plateau. This paper studies the dynamic CFA during changes in perfusion pressure, which attains critical clinical importance in patients with stroke, traumatic brain injury and neurodegenerative disease with a cerebrovascular component. Mathematical and computational models have been used to advance our quantitative understanding of dynamic CFA and to elucidate the underlying physiological mechanisms by analyzing the relation between beat-to-beat data of mean arterial blood pressure (viewed as input) and mean cerebral blood flow velocity(viewed as output) of a putative CFA system. Although previous studies have shown that the dynamic CFA process is nonlinear, most modeling studies to date have been linear. It has also been shown that blood CO2 tension affects the CFA process. This paper presents a nonlinear modeling methodology that includes the dynamic effects of CO2 tension (or its surrogate, end-tidal CO2) as a second input and quantifies CFA from short data-records of healthy human subjects by use of the modeling concept of Principal Dynamic Modes (PDMs). The PDMs improve the robustness of the obtained nonlinear models and facilitate their physiological interpretation. The results demonstrate the importance of including the CO2 input in the dynamic CFA study and the utility of nonlinear models under hypercapnic or hypocapnic conditions

Deletion within the Src homology domain 3 of Bruton's tyrosine kinase resulting in X-linked agammaglobulinemia (XLA).

The gene responsible for X-linked agammaglobulinemia (XLA) has been recently identified to code for a cytoplasmic tyrosine kinase (Bruton's agammaglobulinemia tyrosine kinase, BTK), required for normal B cell development. BTK, like many other cytoplasmic tyrosine kinases, contains Src homology domains (SH2 and SH3), and catalytic kinase domain. SH3 domains are important for the targeting of signaling molecules to specific subcellular locations. We have identified a family with XLA whose affected members have a point mutation (g-->a) at the 5' splice site of intron 8, resulting in the skipping of coding exon 8 and loss of 21 amino acids forming the COOH-terminal portion of the BTK SH3 domain. The study of three generations within this kinship, using restriction fragment length polymorphism and DNA analysis, allowed identification of the mutant X chromosome responsible for XLA and the carrier status in this family. BTK mRNA was present in normal amounts in Epstein-Barr virus-induced B lymphoblastoid cell lines established from affected family members. Although the SH3 deletion did not alter BTK protein stability and kinase activity of the truncated BTK protein was normal, the affected patients nevertheless have a severe B cell defect characteristic for XLA. The mutant protein was modeled using the normal BTK SH3 domain. The deletion results in loss of two COOH-terminal beta strands containing several residues critical for the formation of the putative SH3 ligand-binding pocket. We predict that, as a result, one or more crucial SH3 binding proteins fail to interact with BTK, interrupting the cytoplasmic signal transduction process required for B cell differentiation

Accelerated in vivo proliferation of memory phenotype CD4+ T-cells in human HIV-1 infection irrespective of viral chemokine co-receptor tropism.

CD4(+) T-cell loss is the hallmark of HIV-1 infection. CD4 counts fall more rapidly in advanced disease when CCR5-tropic viral strains tend to be replaced by X4-tropic viruses. We hypothesized: (i) that the early dominance of CCR5-tropic viruses results from faster turnover rates of CCR5(+) cells, and (ii) that X4-tropic strains exert greater pathogenicity by preferentially increasing turnover rates within the CXCR4(+) compartment. To test these hypotheses we measured in vivo turnover rates of CD4(+) T-cell subpopulations sorted by chemokine receptor expression, using in vivo deuterium-glucose labeling. Deuterium enrichment was modeled to derive in vivo proliferation (p) and disappearance (d*) rates which were related to viral tropism data. 13 healthy controls and 13 treatment-naive HIV-1-infected subjects (CD4 143-569 cells/ul) participated. CCR5-expression defined a CD4(+) subpopulation of predominantly CD45R0(+) memory cells with accelerated in vivo proliferation (p = 2.50 vs 1.60%/d, CCR5(+) vs CCR5(-); healthy controls; P<0.01). Conversely, CXCR4 expression defined CD4(+) T-cells (predominantly CD45RA(+) naive cells) with low turnover rates. The dominant effect of HIV infection was accelerated turnover of CCR5(+)CD45R0(+)CD4(+) memory T-cells (p = 5.16 vs 2.50%/d, HIV vs controls; P<0.05), naïve cells being relatively unaffected. Similar patterns were observed whether the dominant circulating HIV-1 strain was R5-tropic (n = 9) or X4-tropic (n = 4). Although numbers were small, X4-tropic viruses did not appear to specifically drive turnover of CXCR4-expressing cells (p = 0.54 vs 0.72 vs 0.44%/d in control, R5-tropic, and X4-tropic groups respectively). Our data are most consistent with models in which CD4(+) T-cell loss is primarily driven by non-specific immune activation