215 research outputs found
MHSP in reversed-biased operation mode for ion blocking in gas-avalanche multipliers
We present recent results on the operation of gas-avalanche detectors
comprising a cascade of gas electron multipliers (GEMs) and Micro-Hole and
Strip Plates (MHSPs) multiplier operated in reversed-bias (R-MHSP) mode. The
operation mechanism of the R-MHSP is explained and its potential contribution
to ion-backflow (IBF) reduction is demonstrated. IBF values of 4E-3 were
obtained in cascaded R-MHSP and GEM multipliers at gains of about 1E+4, though
at the expense of reduced effective gain in the first R- MHSP multiplier in the
cascade.Comment: 23 pages, 8 figure
Detectors for Energy-Resolved Fast Neutron Imaging
Two detectors for energy-resolved fast-neutron imaging in pulsed broad-energy
neutron beams are presented. The first one is a neutron-counting detector based
on a solid neutron converter coupled to a gaseous electron multiplier (GEM).
The second is an integrating imaging technique, based on a scintillator for
neutron conversion and an optical imaging system with fast framing capability
A proposal for a high performance -camera based on liquid Xenon converter and gaseous photomultiplier for PET
présenté par D. Ther
Enumerating Isolated Cliques in Temporal Networks
Isolation is a concept from the world of clique enumeration that is mostly
used to model communities that do not have much contact to the outside world.
Herein, a clique is considered isolated if it has few edges connecting it to
the rest of the graph. Motivated by recent work on enumerating cliques in
temporal networks, we lift the isolation concept to this setting. We discover
that the addition of the time dimension leads to six distinct natural isolation
concepts. Our main contribution is the development of fixed-parameter
enumeration algorithms for five of these six clique types employing the
parameter "degree of isolation". On the empirical side, we implement and test
these algorithms on (temporal) social network data, obtaining encouraging
preliminary results
Stimulus-dependent maximum entropy models of neural population codes
Neural populations encode information about their stimulus in a collective
fashion, by joint activity patterns of spiking and silence. A full account of
this mapping from stimulus to neural activity is given by the conditional
probability distribution over neural codewords given the sensory input. To be
able to infer a model for this distribution from large-scale neural recordings,
we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal
extension of the canonical linear-nonlinear model of a single neuron, to a
pairwise-coupled neural population. The model is able to capture the
single-cell response properties as well as the correlations in neural spiking
due to shared stimulus and due to effective neuron-to-neuron connections. Here
we show that in a population of 100 retinal ganglion cells in the salamander
retina responding to temporal white-noise stimuli, dependencies between cells
play an important encoding role. As a result, the SDME model gives a more
accurate account of single cell responses and in particular outperforms
uncoupled models in reproducing the distributions of codewords emitted in
response to a stimulus. We show how the SDME model, in conjunction with static
maximum entropy models of population vocabulary, can be used to estimate
information-theoretic quantities like surprise and information transmission in
a neural population.Comment: 11 pages, 7 figure
Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells
available in PMC 2011 November 01.Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.Human Frontier Science Program (Strasbourg, France)Howard Hughes Medical InstituteBurroughs Wellcome Fund (Career Award at the Scientific Interface
Using the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification
<p>Abstract</p> <p>Background</p> <p>Microarray-based tumor classification is characterized by a very large number of features (genes) and small number of samples. In such cases, statistical techniques cannot determine which genes are correlated to each tumor type. A popular solution is the use of a subset of pre-specified genes. However, molecular variations are generally correlated to a large number of genes. A gene that is not correlated to some disease may, by combination with other genes, express itself.</p> <p>Results</p> <p>In this paper, we propose a new classiification strategy that can reduce the effect of over-fitting without the need to pre-select a small subset of genes. Our solution works by taking advantage of the information embedded in the testing samples. We note that a well-defined classification algorithm works best when the data is properly labeled. Hence, our classification algorithm will discriminate all samples best when the testing sample is assumed to belong to the correct class. We compare our solution with several well-known alternatives for tumor classification on a variety of publicly available data-sets. Our approach consistently leads to better classification results.</p> <p>Conclusion</p> <p>Studies indicate that thousands of samples may be required to extract useful statistical information from microarray data. Herein, it is shown that this problem can be circumvented by using the information embedded in the testing samples.</p
Development Trends of White Matter Connectivity in the First Years of Life
The human brain is organized into a collection of interacting networks with specialized functions to support various cognitive functions. Recent research has reached a consensus that the brain manifests small-world topology, which implicates both global and local efficiency at minimal wiring costs, and also modular organization, which indicates functional segregation and specialization. However, the important questions of how and when the small-world topology and modular organization come into existence remain largely unanswered. Taking a graph theoretic approach, we attempt to shed light on this matter by an in vivo study, using diffusion tensor imaging based fiber tractography, on 39 healthy pediatric subjects with longitudinal data collected at average ages of 2 weeks, 1 year, and 2 years. Our results indicate that the small-world architecture exists at birth with efficiency that increases in later stages of development. In addition, we found that the networks are broad scale in nature, signifying the existence of pivotal connection hubs and resilience of the brain network to random and targeted attacks. We also observed, with development, that the brain network seems to evolve progressively from a local, predominantly proximity based, connectivity pattern to a more distributed, predominantly functional based, connectivity pattern. These observations suggest that the brain in the early years of life has relatively efficient systems that may solve similar information processing problems, but in divergent ways
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