Coherent (photon) vs incoherent (current) detection of multidimensional optical signals from single molecules in open junctions

The nonlinear optical response of a current-carrying single molecule coupled to two metal leads and driven by a sequence of impulsive optical pulses with controllable phases and time delays is calculated. Coherent (stimulated, heterodyne) detection of photons and incoherent detection of the optically induced current are compared. Using a diagrammatic Liouville space superoperator formalism, the signals are recast in terms of molecular correlation functions which are then expanded in the many-body molecular states. Two dimensional signals in benzene-1,4-dithiol molecule show cross peaks involving charged states. The correlation between optical and charge current signal is also observed. (C) 2015 AIP Publishing LLC.

A New Algorithm for Nonparametric Sequential Detection

We consider nonparametric sequential hypothesis testing problem when the distribution under the null hypothesis is fully known but the alternate hypothesis corresponds to a general family of distributions. We propose a simple algorithm to address the problem. Its performance is analysed and asymptotic properties are proved. The simulated and analysed performance of the algorithm is compared with an earlier algorithm addressing the same problem with similar assumptions. Finally, we provide a justification for our model motivated by a Cognitive Radio scenario and modify the algorithm for optimizing performance when information about the prior probabilities of occurrence of the two hypotheses is available.

Detection of a quantum particle on a lattice under repeated projective measurements

We consider a quantum particle, moving on a lattice with a tight-binding Hamiltonian, which is subjected to measurements to detect its arrival at a particular chosen set of sites. The projective measurements are made at regular time intervals tau, and we consider the evolution of the wave function until the time a detection occurs. We study the probabilities of its first detection at some time and, conversely, the probability of it not being detected (i.e., surviving) up to that time. We propose a general perturbative approach for understanding the dynamics which maps the evolution operator, which consists of unitary transformations followed by projections, to one described by a non-Hermitian Hamiltonian. For some examples of a particle moving on one-and two-dimensional lattices with one or more detection sites, we use this approach to find exact expressions for the survival probability and find excellent agreement with direct numerical results. A mean-field model with hopping between all pairs of sites and detection at one site is solved exactly. For the one-and two-dimensional systems, the survival probability is shown to have a power-law decay with time, where the power depends on the initial position of the particle. Finally, we show an interesting and nontrivial connection between the dynamics of the particle in our model and the evolution of a particle under a non-Hermitian Hamiltonian with a large absorbing potential at some sites.

Robust Whisper Activity Detection Using Long-Term Log Energy Variation of Sub-Band Signal

The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to-noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD.

Viruses and viral proteins

For more than 30 years X-ray crystallography has been by far the most powerful approach for determining the structures of viruses and viral proteins at atomic resolution. The information provided by these structures, which covers many important aspects of the viral life cycle such as cell-receptor recognition, viral entry, nucleic acid transfer and genome replication, has extensively enriched our vision of the virus world. Many of the structures available correspond to potential targets for antiviral drugs against important human pathogens. This article provides an overview of the current knowledge of different structural aspects of the above-mentioned processes.

Boron and Nitrogen Co-doped Carbon Nanoparticles as Photoluminescent Probes for Selective and Sensitive Detection of Picric Acid

Here, we report the synthesis of boron and nitrogen Co-doped carbon nanoparticles (BN-CNPs) by a hydrothermal method using sucrose, boric acid, and urea as the precursors. The BN-CNPs show excellent photoluminescence with a quantum yield of similar to 14.2% in aqueous solution and can be used as photoluminescent probes for selective and sensitive detection of picric acid (PA). PA quenches the photoluminescence signal remarkably, while other explosives cause a little quenching confirming the high selectivity of BN-CNPs. The sensitivity toward PA sensing is high at pH 7 and increases with temperature. The detection limit as well as the sensitivity are shown to improve by adding NaCl to the PA. The low detection limit can be as low as 10 nM at room temperature and pH 7, which indicates the BN-CNPs are superior as compared to other luminescent probes reported in the literature.

De-DUFing the DUFs: Deciphering distant evolutionary relationships of Domains of Unknown Function using sensitive homology detection methods

Background: In the post-genomic era where sequences are being determined at a rapid rate, we are highly reliant on computational methods for their tentative biochemical characterization. The Pfam database currently contains 3,786 families corresponding to ``Domains of Unknown Function'' (DUF) or ``Uncharacterized Protein Family'' (UPF), of which 3,087 families have no reported three-dimensional structure, constituting almost one-fourth of the known protein families in search for both structure and function. Results: We applied a `computational structural genomics' approach using five state-of-the-art remote similarity detection methods to detect the relationship between uncharacterized DUFs and domain families of known structures. The association with a structural domain family could serve as a start point in elucidating the function of a DUF. Amongst these five methods, searches in SCOP-NrichD database have been applied for the first time. Predictions were classified into high, medium and low-confidence based on the consensus of results from various approaches and also annotated with enzyme and Gene ontology terms. 614 uncharacterized DUFs could be associated with a known structural domain, of which high confidence predictions, involving at least four methods, were made for 54 families. These structure-function relationships for the 614 DUF families can be accessed on-line at http://proline.biochem.iisc.ernet.in/RHD_DUFS/. For potential enzymes in this set, we assessed their compatibility with the associated fold and performed detailed structural and functional annotation by examining alignments and extent of conservation of functional residues. Detailed discussion is provided for interesting assignments for DUF3050, DUF1636, DUF1572, DUF2092 and DUF659. Conclusions: This study provides insights into the structure and potential function for nearly 20 % of the DUFs. Use of different computational approaches enables us to reliably recognize distant relationships, especially when they converge to a common assignment because the methods are often complementary. We observe that while pointers to the structural domain can offer the right clues to the function of a protein, recognition of its precise functional role is still `non-trivial' with many DUF domains conserving only some of the critical residues. It is not clear whether these are functional vestiges or instances involving alternate substrates and interacting partners. Reviewers: This article was reviewed by Drs Eugene Koonin, Frank Eisenhaber and Srikrishna Subramanian.

A carboxy terminal domain of the L protein of rinderpest virus possesses RNA triphosphatase activity - The first enzyme in the viral mRNA capping pathway

The large protein L of negative-sense RNA viruses is a multifunctional protein involved in transcription and replication of genomic RNA. It also possesses enzymatic activities involved in capping and methylation of viral mRNAs. The pathway for mRNA capping followed by the L protein of the viruses in the Morbillivirus genus has not been established, although it has been speculated that these viruses may follow the unconventional capping pathway as has been shown for some viruses of Rhabdoviridae family. We had earlier shown that the large protein L of Rinderpest virus expressed as recombinant L-P complex in insect cells as well as the ribonucleoprotein complex from purified virus possesses RNA triphosphatase (RTPase) and guanylyltransferase activities, in addition to RNA dependent RNA polymerase activity. In the present work, we demonstrate that RTPase as well as nucleoside triphosphatase (NTPase) activities are exhibited by a subdomain of the L protein in the C terminal region (a.a. 1640 1840). The RTPase activity depends absolutely on a divalent cation, either magnesium or manganese. Both the RTPase and NTPase activities of the protein show dual metal specificity. Two mutant proteins having alanine mutations in the glutamic acid residues in motif-A of the RTPase domain did not show RTPase activity, while exhibiting reduced NTPase activity suggesting overlapping active sites for the two enzymatic functions. The RTPase and NTPase activities of the L subdomain resemble those of the Vaccinia capping enzyme D1 and the baculovirus LEF4 proteins. (C) 2015 Elsevier Inc. All rights reserved.

Active dendrites mediate stratified gamma-range coincidence detection in hippocampal model neurons

Hippocampal pyramidal neurons exhibit gamma-phase preference in their spikes, selectively route inputs through gamma frequency multiplexing and are considered part of gamma-bound cell assemblies. How do these neurons exhibit gamma-frequency coincidence detection capabilities, a feature that is essential for the expression of these physiological observations, despite their slow membrane time constant? In this conductance-based modelling study, we developed quantitative metrics for the temporal window of integration/coincidence detection based on the spike-triggered average (STA) of the neuronal compartment. We employed these metrics in conjunction with quantitative measures for spike initiation dynamics to assess the emergence and dependence of coincidence detection and STA spectral selectivity on various ion channel combinations. We found that the presence of resonating conductances (hyperpolarization-activated cyclic nucleotide-gated or T-type calcium), either independently or synergistically when expressed together, led to the emergence of spectral selectivity in the spike initiation dynamics and a significant reduction in the coincidence detection window (CDW). The presence of A-type potassium channels, along with resonating conductances, reduced the STA characteristic frequency and broadened the CDW, but persistent sodium channels sharpened the CDW by strengthening the spectral selectivity in the STA. Finally, in a morphologically precise model endowed with experimentally constrained channel gradients, we found that somatodendritic compartments expressed functional maps of strong theta-frequency selectivity in spike initiation dynamics and gamma-range CDW. Our results reveal the heavy expression of resonating and spike-generating conductances as the mechanism underlying the robust emergence of stratified gamma-range coincidence detection in the dendrites of hippocampal and cortical pyramidal neurons.

Superior Performance of a MoS2-RGO Composite and a Borocarbonitride in the Electrochemical Detection of Dopamine, Uric Acid and Adenine

A MoS2-RGO composite and borocarbonitride (BC5N) have been used as electrodes to selectively detect dopamine and uric acid in the presence of ascorbic acid. Both the electrodes show excellent eletrocatalytic activity towards the detection of dopamine, the detection limits being 0.55 mu M and 2.1 mu M in the case of MoS2-RGO and BCN respectively. MoS2-RGO shows a linear range of current over the 1-110 mu M concentrations of dopamine, while BCN shows over the 2.3-20 mu M range. BCN also exhibits satisfactory performance in the oxidation of uric acid with a detection limit of 3.8 mu M and the linear range from 4 to 40 mu M. The MoS2-RGO has also been used to detect adenine as well.

Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidate pixel is classified as belonging to the lesion boundary, lesion interior or normal tissue based on its descriptor value. The tissue transitions are modeled using a Markov chain to estimate the likelihood of a candidate lesion region. Experimental evaluation on a clinical dataset of 135 images show that the proposed approach can achieve high sensitivity (95 %) with modest (3) false positives per image. The approach achieves very similar results (94 % for 3 false positives) on a completely different clinical dataset of 159 images without retraining, highlighting the robustness of the approach.

A Perspective to Adopt Continuous Dynamic Cognition for Malicious Node Detection in Heterogeneous Networks

The current day networks use Proactive networks for adaption to the dynamic scenarios. The use of cognition technique based on the Observe, Orient, Decide and Act loop (OODA) is proposed to construct proactive networks. The network performance degradation in knowledge acquisition and malicious node presence is a problem that exists. The use of continuous time dynamic neural network is considered to achieve cognition. The variance in service rates of user nodes is used to detect malicious activity in heterogeneous networks. The improved malicious node detection rates are proved through the experimental results presented in this paper. (C) 2015 The Authors. Published by Elsevier B.V.

Reconstruction of the Disassembly Pathway of an Icosahedral Viral Capsid and Shape Determination of Two Successive Intermediates

Viral capsids derived from an icosahedral plant virus widely used in physical and nanotechnological investigations were fully dissociated into dimers by a rapid change of pH. The process was probed in vitro at high spatiotemporal resolution by time-resolved small-angle X-ray scattering using a high brilliance synchrotron source. A powerful custom-made global fitting algorithm allowed us to reconstruct the most likely pathway parametrized by a set of stoichiometric coefficients and to determine the shape of two successive intermediates by ab initio calculations. None of these two unexpected intermediates was previously identified in self-assembly experiments, which suggests that the disassembly pathway is not a mirror image of the assembly pathway. These findings shed new light on the mechanisms and the reversibility of the assembly/disassembly of natural and synthetic virus-based systems. They also demonstrate that both the structure and dynamics of an increasing number of intermediate species become accessible to experiments.

An enzyme-linked immuno focus assay for rapid detection and enumeration, and a newborn mouse model for human non-polio enteroviruses associated with acute diarrhea

We have recently reported significant association of non-polio enteroviruses (NPEVs) with acute and persistent diarrhea (18-21% of total diarrheal cases), and non-diarrheal Increased Frequency of Bowel Movements (IFoBM-ND) (about 29% of the NPEV infections) in children and that the NPEV-associated diarrhea was as significant as rotavirus diarrhea. However, their diarrhea-causing potential is yet to be demonstrated in an animal model system. Since the determination of virus titers by the traditional plaque assay takes 4-7 days, there is a need for development of a rapid method for virus titer determination to facilitate active clinical research on enterovirus-associated diarrhea. The goal of this study is to develop a cell-based rapid detection and enumeration method and to demonstrate the diarrhea-inducing potential of purified and characterized non-polio enteroviruses, which were isolated from diarrheic children. Here we describe generation of monoclonal and polyclonal antibodies against purified strains belonging to different serotypes, and development of an enzyme-linked immuno focus assay (ELIFA) for detection and enumeration of live NPEV particles in clinical and purified virus samples, and a newborn mouse model for NPEV diarrhea. Plaque-purified NPVEs, belonging to different serotypes, isolated from children with diarrhea, were grown in cell culture and purified by isopycnic CsCl density gradient centrifugation. By ELIFA, NPEVs could be detected and enumerated within 12 h post-infection. Our results demonstrated that Coxsackievirus B1 (CVB1) and CVB5 strains, isolated from diarrheic children, induced severe diarrhea in orally-inoculated 9-12 day-old mouse pups, fulfilling Koch's postulates. The methods described here would facilitate studies on NPEV-associated gastrointestinal disease. (C) 2015 Elsevier B.V. All rights reserved.

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.