Gene Expression Profiling of Two Distinct Neuronal Populations in the Rodent Spinal Cord

BACKGROUND: In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. METHODOLOGY/PRINCIPAL FINDINGS: We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50-250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. CONCLUSIONS/SIGNIFICANCE: We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional differences between these two cell populations in generating and transmitting the motor output in the rodent spinal cord

High-compression Baseline Dependent Averaging

Baseline dependent averaging (BDA) can be used to reduce the volume of visibility data significantly. Most current BDA schemes perform (weighted) averaging over a certain time interval. This quickly causes decorrelation due to time averaging. We propose to reduce this decorrelation by representing the visibilities by polynomial coefficients. The high compression made feasible by this approach may cause fast-changing calibration parameters to become undersampled. We propose the Compress-Expand-Compress (CEC) method to mitigate this. All compression and expansion methods proposed herein are very simple and cause negligible computation overhead. We demonstrate the effectiveness of our scheme in a simulation emulating a highdynamic range imaging problem

High-compression Baseline Dependent Averaging

Baseline dependent averaging (BDA) can be used to reduce the volume of visibility data significantly. Most current BDA schemes perform (weighted) averaging over a certain time interval. This quickly causes decorrelation due to time averaging. We propose to reduce this decorrelation by representing the visibilities by polynomial coefficients. The high compression made feasible by this approach may cause fast-changing calibration parameters to become undersampled. We propose the Compress-Expand-Compress (CEC) method to mitigate this. All compression and expansion methods proposed herein are very simple and cause negligible computation overhead. We demonstrate the effectiveness of our scheme in a simulation emulating a highdynamic range imaging problem

Signal Processing for Radio Astronomy

Radio astronomy is known for its very large telescope dishes but is currently making a transition towards the use of a large number of small antennas. For example, the Low Frequency Array, commissioned in 2010, uses about 50 stations each consisting of 96 low band antennas and 768 or 1536 high band antennas. The low-frequency receiving system for the future Square Kilometre Array is envisaged to initially consist of over 131,000 receiving elements and to be expanded later. These instruments pose interesting array signal processing challenges. To present some aspects, we start by describing how the measured correlation data is traditionally converted into an image, and translate this into an array signal processing framework. This paves the way to describe self-calibration and image reconstruction as estimation problems. Self-calibration of the instrument is required to handle instrumental effects such as the unknown, possibly direction dependent, response of the receiving elements, as well a unknown propagation conditions through the Earth’s troposphere and ionosphere. Array signal processing techniques seem well suited to handle these challenges. Interestingly, image reconstruction, calibration and interference mitigation are often intertwined in radio astronomy, turning this into an area with very challenging signal processing problems.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Circuits and System

Signal Processing for Radio Astronomy

Radio astronomy is known for its very large telescope dishes but is currently making a transition towards the use of a large number of small antennas. For example, the Low Frequency Array, commissioned in 2010, uses about 50 stations each consisting of 96 low band antennas and 768 or 1536 high band antennas. The low-frequency receiving system for the future Square Kilometre Array is envisaged to initially consist of over 131,000 receiving elements and to be expanded later. These instruments pose interesting array signal processing challenges. To present some aspects, we start by describing how the measured correlation data is traditionally converted into an image, and translate this into an array signal processing framework. This paves the way to describe self-calibration and image reconstruction as estimation problems. Self-calibration of the instrument is required to handle instrumental effects such as the unknown, possibly direction dependent, response of the receiving elements, as well a unknown propagation conditions through the Earth’s troposphere and ionosphere. Array signal processing techniques seem well suited to handle these challenges. Interestingly, image reconstruction, calibration and interference mitigation are often intertwined in radio astronomy, turning this into an area with very challenging signal processing problems

The brightness and spatial distributions of terrestrial radio sources

Faint undetected sources of radio-frequency interference (RFI) might become visible in long radio observations when they are consistently present over time. Thereby, they might obstruct the detection of the weak astronomical signals of interest. This issue is especially important for Epoch of Reionisation (EoR) projects that try to detect the faint redshifted HI signals from the time of the earliest structures in the Universe. We explore the RFI situation at 30-163 MHz by studying brightness histograms of visibility data observed with LOFAR, similar to radio-source-count analyses that are used in cosmology. An empirical RFI distribution model is derived that allows the simulation of RFI in radio observations. The brightness histograms show an RFI distribution that follows a power-law distribution with an estimated exponent around -1.5. With several assumptions, this can be explained with a uniform distribution of terrestrial radio sources whose radiation follows existing propagation models. Extrapolation of the power law implies that the current LOFAR EoR observations should be severely RFI limited if the strength of RFI sources remains strong after time integration. This is in contrast with actual observations, which almost reach the thermal noise and are thought not to be limited by RFI. Therefore, we conclude that it is unlikely that there are undetected RFI sources that will become visible in long observations. Consequently, there is no indication that RFI will prevent an EoR detection with LOFAR

Upper limits on the 21 cm epoch of reionization power spectrum from one night with LOFAR

We present the first limits on the Epoch of Reionization 21 cm H i power spectra, in the redshift range z = 7.9–10.6, using the Low-Frequency Array (LOFAR) High-Band Antenna (HBA). In total, 13.0 hr of data were used from observations centered on the North Celestial Pole. After subtraction of the sky model and the noise bias, we detect a non-zero ${{\rm{\Delta }}}_{{\rm{I}}}^{2}={(56\pm 13\mathrm{mK})}^{2}$ (1-σ) excess variance and a best 2-σ upper limit of ${{\rm{\Delta }}}_{21}^{2}\lt {(79.6\mathrm{mK})}^{2}$ at k = 0.053 h cMpc−1 in the range z = 9.6–10.6. The excess variance decreases when optimizing the smoothness of the direction- and frequency-dependent gain calibration, and with increasing the completeness of the sky model. It is likely caused by (i) residual side-lobe noise on calibration baselines, (ii) leverage due to nonlinear effects, (iii) noise and ionosphere-induced gain errors, or a combination thereof. Further analyses of the excess variance will be discussed in forthcoming publications