770 research outputs found
Emerging concepts in pancreatic cancer medicine: targeting the tumor stroma
Pancreatic ductal adenocarcinoma is a stroma-rich and highly challenging cancer to treat. Over recent years, it has become increasingly evident that the complex network of soluble cytokines, growth factors, proteases, and components of the extracellular matrix collaboratively interact within the tumor microenvironment, sustaining and driving cancer cell proliferation, invasion, and early metastasis. More recently, the tumor microenvironment has also been appreciated to mediate therapeutic resistance in pancreatic ductal adenocarcinoma, thus opening numerous avenues for novel therapeutic explorations. Inert and soluble components of the tumor stroma have been targeted in order to break down the extracellular matrix scaffold, relieve vessel compression, and increase drug delivery to hypovascular tumors. Moreover, targeting of antiapoptotic, immunosuppressive, and pro-proliferative effects of the tumor stroma provides novel vantage points of attack. This review focuses on current and future developments in pancreatic cancer medicine, with a particular emphasis on biophysical and biochemical approaches that target the tumor microenvironment
Search for Sub-TeV Gamma Rays Coincident with BATSE Gamma Ray Bursts
Project GRAND is a 100m x 100m air shower array of proportional wire chambers
(PWCs). There are 64 stations each with eight 1.29 m^2 PWC planes arranged in
four orthogonal pairs placed vertically above one another to geometrically
measure the angles of charged secondaries. A steel plate above the bottom pair
of PWCs differentiates muons (which pass undeflected through the steel) from
non-penetrating particles. FLUKA Monte Carlo studies show that a TeV gamma ray
striking the atmosphere at normal incidence produces 0.23 muons which reach
ground level where their angles and identities are measured. Thus,
paradoxically, secondary muons are used as a signature for gamma ray primaries.
The data are examined for possible angular and time coincidences with eight
gamma ray bursts (GRBs) detected by BATSE. Seven of the GRBs were selected
because of their good acceptance by GRAND and high BATSE Fluence. The eighth
GRB was added due to its possible coincident detection by Milagrito. For each
of the eight candidate GRBs, the number of excess counts during the BATSE T90
time interval and within plus or minus five degrees of BATSE's direction was
obtained. The highest statistical significance reported in this paper (2.7
sigma) is for the event that was predicted to be the most likely to be observed
(GRB 971110).Comment: To be presented at the XXVIII International Cosmic Ray Conference,
Tsukuba, Japa
"Last-Mile" preparation for a potential disaster
Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of small-scale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socio-economic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity
Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex
The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which
measures the radio emission of the cosmic-ray air-showers in the frequency band
of 30-80 MHz. Tunka-Rex is co-located with TAIGA experiment in Siberia and
consists of 63 antennas, 57 of them are in a densely instrumented area of about
1 km\textsuperscript{2}. In the present work we discuss the improvements of the
signal reconstruction applied for the Tunka-Rex. At the first stage we
implemented matched filtering using averaged signals as template. The
simulation study has shown that matched filtering allows one to decrease the
threshold of signal detection and increase its purity. However, the maximum
performance of matched filtering is achievable only in case of white noise,
while in reality the noise is not fully random due to different reasons. To
recognize hidden features of the noise and treat them, we decided to use
convolutional neural network with autoencoder architecture. Taking the recorded
trace as an input, the autoencoder returns denoised trace, i.e. removes all
signal-unrelated amplitudes. We present the comparison between standard method
of signal reconstruction, matched filtering and autoencoder, and discuss the
prospects of application of neural networks for lowering the threshold of
digital antenna arrays for cosmic-ray detection.Comment: ARENA2018 proceeding
Current Status and New Challenges of The Tunka Radio Extension
The Tunka Radio Extension (Tunka-Rex) is an antenna array spread over an area
of about 1~km. The array is placed at the Tunka Advanced Instrument for
cosmic rays and Gamma Astronomy (TAIGA) and detects the radio emission of air
showers in the band of 30 to 80~MHz. During the last years it was shown that a
sparse array such as Tunka-Rex is capable of reconstructing the parameters of
the primary particle as accurate as the modern instruments. Based on these
results we continue developing our data analysis. Our next goal is the
reconstruction of cosmic-ray energy spectrum observed only by a radio
instrument. Taking a step towards it, we develop a model of aperture of our
instrument and test it against hybrid TAIGA observations and Monte-Carlo
simulations. In the present work we give an overview of the current status and
results for the last five years of operation of Tunka-Rex and discuss prospects
of the cosmic-ray energy estimation with sparse radio arrays.Comment: Proceedings of E+CRS 201
Improved measurements of the energy and shower maximum of cosmic rays with Tunka-Rex
The Tunka Radio Extension (Tunka-Rex) is an array of 63 antennas located in
the Tunka Valley, Siberia. It detects radio pulses in the 30-80 MHz band
produced during the air-shower development. As shown by Tunka-Rex, a sparse
radio array with about 200 m spacing is able to reconstruct the energy and the
depth of the shower maximum with satisfactory precision using simple methods
based on parameters of the lateral distribution of amplitudes. The LOFAR
experiment has shown that a sophisticated treatment of all individually
measured amplitudes of a dense antenna array can make the precision comparable
with the resolution of existing optical techniques. We develop these ideas
further and present a method based on the treatment of time series of measured
signals, i.e. each antenna station provides several points (trace) instead of a
single one (amplitude or power). We use the measured shower axis and energy as
input for CoREAS simulations: for each measured event we simulate a set of
air-showers with proton, helium, nitrogen and iron as primary particle (each
primary is simulated about ten times to cover fluctuations in the shower
maximum due to the first interaction). Simulated radio pulses are processed
with the Tunka-Rex detector response and convoluted with the measured signals.
A likelihood fit determines how well the simulated event fits to the measured
one. The positions of the shower maxima are defined from the distribution of
chi-square values of these fits. When using this improved method instead of the
standard one, firstly, the shower maximum of more events can be reconstructed,
secondly, the resolution is increased. The performance of the method is
demonstrated on the data acquired by the Tunka-Rex detector in 2012-2014.Comment: Proceedings of the 35th ICRC 2017, Busan, Kore
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