ANRC Hybrid Test Bed Implementation and an End-to-End Performance Characterization of Dynamic Spectrum Access

An abundance of spectrum access and sensing algorithms are available in the dynamic spectrum access (DSA) and cognitive radio (CR) literature. Often, however, the functionality and performance of such algorithms are validated against theoretical calculations using only simulations. Both the theoretical calculations and simulations come with their attendant sets of assumptions. For instance, designers of dynamic spectrum access algorithms often take spectrum sensing and rendezvous mechanisms between transmitter-receiver pairs for granted. Test bed designers, on the other hand, either customize so much of their design that it becomes difficult to replicate using commercial off the shelf (COTS) components or restrict themselves to simulation, emulation /hardware-in-Ioop (HIL), or pure hardware but not all three. Implementation studies on test beds sophisticated enough to combine the three aforementioned aspects, but at the same time can also be put together using COTS hardware and software packages are rare. In this paper we describe i) the implementation of a hybrid test bed using a previously proposed hardware agnostic system architecture ii) the implementation of DSA on this test bed, and iii) the realistic hardware and software-constrained performance of DSA. Snapshot energy detector (ED) and Cumulative Summation (CUSUM), a sequential change detection algorithm, are available for spectrum sensing and a two-way handshake mechanism in a dedicated control channel facilitates transmitter-receiver rendezvous.

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Efficient Low Complexity Power Allocation Policies for Wireless Communication Systems Guaranteeing QoS

We consider near-optimal policies for a single user transmitting on a wireless channel which minimize average queue length under average power constraint. The power is consumed in transmission of data only. We consider the case when the power used in transmission is a linear function of the data transmitted. The transmission channel may experience multipath fading. Later, we also extend these results to the multiuser case. We show that our policies can be used in a system with energy harvesting sources at the transmitter. Next we consider data users which require minimum rate guarantees. Finally we consider the system which has both data and real time users. Our policies have low computational complexity, closed form expression for mean delays and require only the mean arrival rate with no queue length information.

Secure Communication in Amplify-and-Forward Networks with Multiple Eavesdroppers: Decoding with SNR Thresholds

The problem of secure unicast communication over a two hop Amplify-and-Forward wireless relay network with multiple eavesdroppers is considered. Assuming that a receiver (destination or eavesdropper) can decode a message only if the received SNR is above a predefined threshold, we consider this problem in two scenarios. In the first scenario, we maximize the SNR at the legitimate destination, subject to the condition that the received SNR at each eavesdropper is below the target threshold. Due to the non-convex nature of the objective function and eavesdroppers' constraints, we transform variables and obtain a quadratically constrained quadratic program (QCQP) with convex constraints, which can be solved efficiently. When the constraints are not convex, we consider a semidefinite relaxation (SDR) to obtain computationally efficient approximate solution. In the second scenario, we minimize the total power consumed by all relay nodes, subject to the condition that the received SNR at the legitimate destination is above the threshold and at every eavesdropper, it is below the corresponding threshold. We propose a semidefinite relaxation of the problem in this scenario and also provide an analytical lower bound.

Chemical Shifts to Metabolic Pathways: Identifying Metabolic Pathways Directly from a Single 2D NMR Spectrum

Identifying cellular processes in terms of metabolic pathways is one of the avowed goals of metabolomics studies. Currently, this is done after relevant metabolites are identified to allow their mapping onto specific pathways. This task is daunting due to the complex nature of cellular processes and the difficulty in establishing the identity of individual metabolites. We propose here a new method: ChemSMP (Chemical Shifts to Metabolic Pathways), which facilitates rapid analysis by identifying the active metabolic pathways directly from chemical shifts obtained from a single two-dimensional (2D) C-13-H-1] correlation NMR spectrum without the need for identification and assignment of individual metabolites. ChemSMP uses a novel indexing and scoring system comprised of a ``uniqueness score'' and a ``coverage score''. Our method is demonstrated on metabolic pathways data from the Small Molecule Pathway Database (SMPDB) and chemical shifts from the Human Metabolome Database (HMDB). Benchmarks show that ChemSMP has a positive prediction rate of >90% in the presence of deduttered data and can sustain the same at 60-70% even in the presence of noise, such as deletions of peaks and chemical shift deviations. The method tested on NMR data acquired for a mixture of 20 amino acids shows a success rate of 93% in correct recovery of pathways. When used on data obtained from the cell lysate of an unexplored oncogenic cell line, it revealed active metabolic pathways responsible for regulating energy homeostasis of cancer cells. Our unique tool is thus expected to significantly enhance analysis of NMIR-based metabolomics data by reducing existing impediments.

Potency and pharmacokinetics of broad spectrum and isoform-specific p110 gamma and delta inhibitors in cancers

Emerging data on cancer suggesting that target-based therapy is promising strategy in cancer treatment. PI3K-AKT pathway is extensively studied in many cancers; several inhibitors target this pathway in different levels. Recent finding on this pathway uncovered the therapeutic applications of PI3K-specific inhibitors; PI3K, AKT, and mTORC broad spectrum inhibitors. Noticeably, class I PI3K isoforms, p110 and p110 catalytic subunits have rational therapeutic application than other isoforms. Therefore, three classes of inhibitors: isoform-specific, dual-specific and broad spectrum were selected for molecular docking and dynamics. First, p110 structure was modelled; active site was analyzed. Then, molecular docking of each class of inhibitors were studied; the docked complexes were further used in 1.2ns molecular dynamics simulation to report the potency of each class of inhibitor. Remarkably, both the studies retained the similar kind of protein ligand interactions. GDC-0941, XL-147 (broad spectrum); TG100-115 (dual-specific); and AS-252424, PIK-294 (isoform-specific) were found to be potential inhibitors of p110 and p110, respectively. In addition to that pharmacokinetic properties are within recommended ranges. Finally, molecular phylogeny revealed that p110 and p110 are evolutionarily divergent; they probably need separate strategies for drug development.

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.

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.

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.

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.

A dynamic view of the spread and intracellular distribution of Salmonella enterica.

The events that determine the dynamics of proliferation, spread and distribution of microbial pathogens within their hosts are surprisingly heterogeneous and poorly understood. We contend that understanding these phenomena at a sophisticated level with the help of mathematical models is a prerequisite for the development of truly novel, targeted preventative measures and drug regimes. We describe here recent studies of Salmonella enterica infections in mice which suggest that bacteria resist the antimicrobial environment inside host cells and spread to new sites, where infection foci develop, and thus avoid local escalation of the adaptive immune response. We further describe implications for our understanding of the pathogenic mechanism inside the host.