Elucidating the cancer-specific genetic alteration spectrum of glioblastoma derived cell lines from whole exome and RNA sequencing

Cell lines derived from tumor tissues have been used as a valuable system to study gene regulation and cancer development. Comprehensive characterization of the genetic background of cell lines could provide clues on novel genes responsible for carcinogenesis and help in choosing cell lines for particular studies. Here, we have carried out whole exome and RNA sequencing of commonly used glioblastoma (GBM) cell lines (U87, T98G, LN229, U343, U373 and LN18) to unearth single nucleotide variations (SNVs), indels, differential gene expression, gene fusions and RNA editing events. We obtained an average of 41,071 SNVs out of which 1,594 (3.88%) were potentially cancer-specific. The cell lines showed frequent SNVs and indels in some of the genes that are known to be altered in GBM-EGFR, TP53, PTEN, SPTA1 and NF1. Chromatin modifying genes-ATRX, MLL3, MLL4, SETD2 and SRCAP also showed alterations. While no cell line carried IDH1 mutations, five cell lines showed hTERT promoter activating mutations with a concomitant increase in hTERT transcript levels. Five significant gene fusions were found of which NUP93-CYB5B was validated. An average of 18,949 RNA editing events was also obtained. Thus we have generated a comprehensive catalogue of genetic alterations for six GBM cell lines.

Numerical Investigations for Leakage and Windage Heating in Straight-Through Labyrinth Seals

The ability to quantify leakage flow and windage heating for labyrinth seals with honeycomb lands is critical in understanding gas turbine engine system performance and predicting its component life. Variety of labyrinth seal configurations (number of teeth, stepped or straight, honeycomb cell size) are in use in gas turbines, and for each configuration, there are many geometric factors that can impact a seal's leakage and windage characteristics. This paper describes the development of a numerical methodology aimed at studying the effect of honeycomb lands on leakage and windage heating. Specifically, a three-dimensional computational fluid dynamics (CFD) model is developed utilizing commercial finite volume-based software incorporating the renormalization group (RNG) k-epsilon turbulence model with modified Schmidt number. The modified turbulence model is benchmarked and fine-tuned based on several experiments. Using this model, a broad parametric study is conducted by varying honeycomb cell size, pressure ratio (PR), and radial clearance for a four-tooth straight-through labyrinth seal. The results show good agreement with available experimental data. They further indicate that larger honeycomb cells predict higher seal leakage and windage heating at tighter clearances compared to smaller honeycomb cells and smooth lands. However, at open seal clearances larger honeycomb cells have lower leakage compared to smaller honeycomb cells.

Time-Instant Sampling Based Encoding of Time-Varying Acoustic Spectrum

The inner ear has been shown to characterize an acoustic stimuli by transducing fluid motion in the inner ear to mechanical bending of stereocilia on the inner hair cells (IHCs). The excitation motion/energy transferred to an IHC is dependent on the frequency spectrum of the acoustic stimuli, and the spatial location of the IHC along the length of the basilar membrane (BM). Subsequently, the afferent auditory nerve fiber (ANF) bundle samples the encoded waveform in the IHCs by synapsing with them. In this work we focus on sampling of information by afferent ANFs from the IHCs, and show computationally that sampling at specific time instants is sufficient for decoding of time-varying acoustic spectrum embedded in the acoustic stimuli. The approach is based on sampling the signal at its zero-crossings and higher-order derivative zero-crossings. We show results of the approach on time-varying acoustic spectrum estimation from cricket call signal recording. The framework gives a time-domain and non-spatial processing perspective to auditory signal processing. The approach works on the full band signal, and is devoid of modeling any bandpass filtering mimicking the BM action. Instead, we motivate the approach from the perspective of event-triggered sampling by afferent ANFs on the stimuli encoded in the IHCs. Though the approach gives acoustic spectrum estimation but it is shallow on its complete understanding for plausible bio-mechanical replication with current mammalian auditory mechanics insights.

Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.

Distributed Nonparametric Sequential Spectrum Sensing under Electromagnetic Interference

We propose a distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up. Two or more local nodes make decisions and inform the fusion centre (FC) over a reporting Multiple Access Channel (MAC), which then makes the final decision. The local nodes use energy detection and the FC uses mean detection in the presence of fading, heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of the primary signal, channel gain and the EMI is not known. Different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the Fe. Modification of a recently developed random walk test is selected for the local nodes for energy detection as well as at the fusion centre for mean detection. We show via simulations and analysis that the nonparametric distributed algorithm developed performs well in the presence of fading, EMI and outliers. The algorithm is iterative in nature making the computation and storage requirements minimal.

Broad spectrum measurement of the birefringence of an isothiocyanate based liquid crystal.

Tunable materials with high anisotropy of refractive index and low loss are of particular interest in the microwave and terahertz range. Nematic liquid crystals are highly sensitive to electric and magnetic fields and may be designed to have particularly high birefringence. In this paper we investigate birefringence and absorption losses in an isothiocyanate based liquid crystal (designed for high anisotropy) in a broad range of the electromagnetic spectrum, namely 0.1-4 GHz, 30 GHz, 0.5-1.8 THz, and in the visible and near-infrared region (400 nm-1600 nm). We report high birefringence (Δn = 0.19-0.395) and low loss in this material. This is attractive for tunable microwave and terahertz device applications.

Continuous spectrum growth and modal instability in swirling duct flow

We present results on the stability of compressible inviscid swirling flows in an annular duct. Such flows are present in aeroengines, for example in the by-pass duct, and there are also similar flows in many aeroacoustic or aeronautical applications. The linearised Euler equations have a ('critical layer') singularity associated with pure convection of the unsteady disturbance by the mean flow, and we focus our attention on this region of the spectrum. By considering the critical layer singularity, we identify the continuous spectrum of the problem and describe how it contributes to the unsteady field. We find a very generic family of instability modes near to the continuous spectrum, whose eigenvalue wavenumbers form an infinite set and accumulate to a point in the complex plane. We study this accumulation process asymptotically, and find conditions on the flow to support such instabilities. It is also found that the continuous spectrum can cause a new type of instability, leading to algebraic growth with an exponent determined by the mean flow, given in the analysis. The exponent of algebraic growth can be arbitrarily large. Numerical demonstrations of the continuous spectrum instability, and also the modal instabilities are presented.

Raman Spectrum of silicon nanowires

We measure the effects of phonon confinement on the Raman spectra of silicon nanowires (SiNWs). We show how previous reports of phonon confinement in SiNWs and nanostructures are actually inconsistent with phonon confinement, but are due to the intense local heating caused by the laser power used for Raman measurements. This is peculiar to nanostructures, and would require orders of magnitude higher power in bulk Si. By varying the temperature, power and excitation energy, we identify the contributions of pure confinement, heating and carrier photo-excitation. After eliminating laser-related effects, the Raman spectra show confinement signatures typical of quantum wires. © 2003 Elsevier B.V. All rights reserved.