1,157 research outputs found
Matrix metalloproteinase-9 activity and a downregulated Hedgehog pathway impair blood-brain barrier function in an <i>in vitro</i> model of CNS tuberculosis
Central nervous system tuberculosis (CNS TB) has a high mortality and morbidity associated with severe inflammation. The blood-brain barrier (BBB) protects the brain from inflammation but the mechanisms causing BBB damage in CNS TB are uncharacterized. We demonstrate that Mycobacterium tuberculosis (Mtb) causes breakdown of type IV collagen and decreases tight junction protein (TJP) expression in a co-culture model of the BBB. This increases permeability, surface expression of endothelial adhesion molecules and leukocyte transmigration. TJP breakdown was driven by Mtb-dependent secretion of matrix metalloproteinase (MMP)-9. TJP expression is regulated by Sonic hedgehog (Shh) through transcription factor Gli-1. In our model, the hedgehog pathway was downregulated by Mtb-stimulation, but Shh levels in astrocytes were unchanged. However, Scube2, a glycoprotein regulating astrocyte Shh release was decreased, inhibiting Shh delivery to brain endothelial cells. Activation of the hedgehog pathway by addition of a Smoothened agonist or by addition of exogenous Shh, or neutralizing MMP-9 activity, decreased permeability and increased TJP expression in the Mtb-stimulated BBB co-cultures. In summary, the BBB is disrupted by downregulation of the Shh pathway and breakdown of TJPs, secondary to increased MMP-9 activity which suggests that these pathways are potential novel targets for host directed therapy in CNS TB
Computational framework for applying electrical impedance tomography to head imaging
This work introduces a computational framework for applying absolute
electrical impedance tomography to head imaging without accurate information on
the head shape or the electrode positions. A library of fifty heads is employed
to build a principal component model for the typical variations in the shape of
the human head, which leads to a relatively accurate parametrization for head
shapes with only a few free parameters. The estimation of these shape
parameters and the electrode positions is incorporated in a regularized
Newton-type output least squares reconstruction algorithm. The presented
numerical experiments demonstrate that strong enough variations in the internal
conductivity of a human head can be detected by absolute electrical impedance
tomography even if the geometric information on the measurement configuration
is incomplete to an extent that is to be expected in practice.Comment: 25 pages, 12 figure
The regularized monotonicity method: detecting irregular indefinite inclusions
In inclusion detection in electrical impedance tomography, the support of
perturbations (inclusion) from a known background conductivity is typically
reconstructed from idealized continuum data modelled by a Neumann-to-Dirichlet
map. Only few reconstruction methods apply when detecting indefinite
inclusions, where the conductivity distribution has both more and less
conductive parts relative to the background conductivity; one such method is
the monotonicity method of Harrach, Seo, and Ullrich. We formulate the method
for irregular indefinite inclusions, meaning that we make no regularity
assumptions on the conductivity perturbations nor on the inclusion boundaries.
We show, provided that the perturbations are bounded away from zero, that the
outer support of the positive and negative parts of the inclusions can be
reconstructed independently. Moreover, we formulate a regularization scheme
that applies to a class of approximative measurement models, including the
Complete Electrode Model, hence making the method robust against modelling
error and noise. In particular, we demonstrate that for a convergent family of
approximative models there exists a sequence of regularization parameters such
that the outer shape of the inclusions is asymptotically exactly characterized.
Finally, a peeling-type reconstruction algorithm is presented and, for the
first time in literature, numerical examples of monotonicity reconstructions
for indefinite inclusions are presented.Comment: 28 pages, 7 figure
Levenberg-Marquardt algorithm for acousto-electric tomography based on the complete electrode model
The inverse problem in Acousto-Electric tomography concerns the
reconstruction of the electric conductivity in a domain from knowledge of the
power density function in the interior of the body. This interior power density
results from currents prescribed at boundary electrodes (and can be obtained
through electro-static boundary measurements together with auxiliary acoustic
measurement. In Electrical Impedance Tomography, the complete electrode model
is known to be the most accurate model for the forward modelling. In this
paper, the reconstruction problem of Acousto-Electric tomography is posed using
the (smooth) complete electrode model, and a Levenberg-Marquardt iteration is
formulated in appropriate function spaces. This results in a system of partial
differential equations to be solved in each iteration. To increase the
computational efficiency and stability, a strategy based on both the complete
electrode model and the continuum model with Dirichlet boundary condition is
proposed. The system of equations is implemented numerically for a two
dimensional scenario and the algorithm is tested on two different numerical
phantoms, a heart and lung model and a human brain model. Several numerical
experiments are carried out confirming the feasibility, accuracy and stability
of the methods
Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms
Recognizing sEMG (Surface Electromyography) signals belonging to a particular
action (e.g., lateral arm raise) automatically is a challenging task as EMG
signals themselves have a lot of variation even for the same action due to
several factors. To overcome this issue, there should be a proper separation
which indicates similar patterns repetitively for a particular action in raw
signals. A repetitive pattern is not always matched because the same action can
be carried out with different time duration. Thus, a depth sensor (Kinect) was
used for pattern identification where three joint angles were recording
continuously which is clearly separable for a particular action while recording
sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving
Dynamic Time Warping) approach is introduced. This technique is allowed to
retrieve suspected motion of interest from raw signals. MDTW based on DTW
algorithm, but it will be moving through the whole dataset in a pre-defined
manner which is capable of picking up almost all the suspected segments inside
a given dataset an optimal way. Elevated bicep curl and lateral arm raise
movements are taken as motions of interest to show how the proposed technique
can be employed to achieve auto identification and labelling. The full
implementation is available at https://github.com/GPrathap/OpenBCIPytho
Series reversion for practical electrical impedance tomography with modeling errors
This work extends the results of [Garde and Hyv\"onen, Math. Comp.
91:1925-1953] on series reversion for Calder\'on's problem to the case of
realistic electrode measurements, with both the internal admittivity of the
investigated body and the contact admittivity at the electrode-object
interfaces treated as unknowns. The forward operator, sending the internal and
contact admittivities to the linear electrode current-to-potential map, is
first proven to be analytic. A reversion of the corresponding Taylor series
yields a family of numerical methods of different orders for solving the
inverse problem of electrical impedance tomography, with the possibility to
employ different parametrizations for the unknown internal and boundary
admittivities. The functionality and convergence of the methods is established
only if the employed finite-dimensional parametrization of the unknowns allows
the Fr\'echet derivative of the forward map to be injective, but we also
heuristically extend the methods to more general settings by resorting to
regularization motivated by Bayesian inversion. The performance of this
regularized approach is tested via three-dimensional numerical examples based
on simulated data. The effect of modeling errors is a focal point of the
numerical studies.Comment: 24 pages, 3 figure
Recommended from our members
Evaluating Experimental Design of ERT for Soil Moisture Monitoring in Contour Hedgerow Intercropping Systems
Contour hedgerow intercropping systems have been proposed as an alternative to traditional agricultural practice with a single crop, as they are effective in reducing run-off and soil erosion. However, competition for water and nutrients between crops and associated hedgerows may reduce the overall performance of these systems. To get a more detailed understanding of the competition for water, spatially resolved monitoring of soil water contents in the soil-plant-atmosphere system is necessary. Electrical resistivity tomography (ERT) is potentially a valuable technique to monitor changes in soil moisture in space and time. In this study, the performance of different ERT electrode arrays to detect the soil moisture dynamics in a mono- and an intercropping system was tested. Their performance was analyzed based on a synthetic study using geophysical measures, such as data recovery and resolution, and using spatial statistics of retrieved water content, such as an adjusted coefficient of variation and semivariances. The synthetic ERT measurements detected differences between the cropping systems and retrieved spatial structure of the soil moisture distribution, but the variance and semivariance were underestimated. Sharp water content contrasts between horizons or in the neighborhood of a root water uptake bulb were smoothened. The addition of electrodes deeper in the soil improved the performance, but sometimes only marginally. ERT is therefore a valuable tool for soil moisture monitoring in the field under different cropping systems if an electrode array is used which can resolve the patterns expected to be present in the medium. The use of spatial statistics allowed to not only identify the overall model recovery, but also to quantify the recovery of spatial structures
- …