Signal Window Research Articles

Bound muon decay spectrum in the leading logarithmic accuracy

We compute the dominant, logarithmically enhanced radiative corrections to the electron spectrum in the bound muon decay in the whole experimentally interesting range. The corrected spectrum fits well the TWIST results. The remaining theoretical error, dominated by the nuclear charge distribution, can be reduced in the muon-electron conversion searches by measuring the spectrum slightly below the New Physics signal window.

Overview of Critical Parameters for the Design and Execution of a High-Throughput Screen for Allosteric Ligands.

Allosteric ligands modulate the activity of receptor targets by binding to sites that are distinct from the orthosteric (native agonist) binding site. Allosteric modulators have potential therapeutic advantages over orthosteric agonists and antagonists, including improved selectivity, and maintenance of the spatial and temporal fidelity of native signaling patterns. The identification of allosteric ligands presents unique challenges because of the requirement for screening in the presence of an orthosteric agonist, the small signal window that is produced by many allosteric modulators, the proclivity of allosteric modulators to exhibit activity switching within a chemotype (e.g., one compound may be a positive allosteric modulator while a close analog is a negative allosteric modulator), and probe dependence (differential interactions with different orthosteric agonists). Described in this unit are emerging strategies for the identification of allosteric ligands by high-throughput screening (HTS), including the use of multiple-add/multiple-read HTS assays and tool molecule-based screening formats. © 2016 by John Wiley & Sons, Inc.

Whole-genome resequencing of Xishuangbanna fighting chicken to identify signatures of selection.

BackgroundSelective breeding for genetic improvement is expected to leave distinctive selection signatures within genomes. The identification of selection signatures can help to elucidate the mechanisms of selection and accelerate genetic improvement. Fighting chickens have undergone extensive artificial selection, resulting in modifications to their morphology, physiology and behavior compared to wild species. Comparing the genomes of fighting chickens and wild species offers a unique opportunity for identifying signatures of artificial selection.ResultsWe identified selection signals in 100-kb windows sliding in 10-kb steps by using two approaches: the pooled heterozygosity ({text{H}}_{text{p}} ) and the fixation index (F_{text{ST}} ) between Xishuangbanna fighting chicken (YNLC) and Red Jungle Fowl. A total of 413 candidate genes were found to be putatively under selection in YNLC. These genes were related to traits such as growth, disease resistance, aggressive behavior and energy metabolism, as well as the morphogenesis and homeostasis of many tissues and organs.ConclusionsThis study reveals mechanisms and targets of artificial selection, which will contribute to improve our knowledge about the evolution of fighting chickens and facilitate future quantitative trait loci mapping.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0239-4) contains supplementary material, which is available to authorized users.

Ketamine-induced changes in connectivity of functional brain networks in awake female nonhuman primates: a translational functional imaging model.

There is a significant interest in the NMDA-receptor antagonist ketamine due to its efficacy in treating depressive disorders and its induction of psychotic-like symptoms that make it a useful tool for modeling psychosis. Pharmacological MRI in awake nonhuman primates provides a highly translational model for studying the brain network dynamics involved in producing these drug effects. The present study evaluated ketamine-induced changes in functional connectivity (FC) in awake rhesus monkeys. The effects of ketamine after pretreatment with the antipsychotic drug risperidone were also examined. Functional MRI scans were conducted in four awake adult female rhesus monkeys during sub-anesthetic i.v. infusions of ketamine (0.345mg/kg bolus followed by 0.256mgkg-1h-1 constant infusion) with and without risperidone pretreatment (0.06mg/kg). A 10-min window of stable BOLD signal was used to compare FC between baseline and drug conditions. FC was assessed in specific regions of interest using seed-based cross-correlation analysis. Ketamine infusion induced extensive changes in FC. In particular, FC to the dorsolateral prefrontal cortex (dlPFC) was increased in several cortical and subcortical regions. Pretreatment with risperidone largely attenuated ketamine-induced changes in FC. The results are highly consistent with similar human imaging studies showing ketamine-induced changes in FC, as well as a significant attenuation of these changes when ketamine infusion is preceded by pretreatment with risperidone. The extensive increases shown in FC to the dlPFC are consistent with the idea that disinhibition of the dlPFC may be a key driver of the antidepressant and psychotomimetic effects of ketamine.

EMPIRICAL STUDY OF TWIN DEFICITS IN INDONESIA: THE RELATIONSHIP BETWEEN CAUSALITY AND EARLY WARNING SYSTEM OF TWIN DEFICITS’ CAUSE

The aim of this research is to investigate the causality of deficit budget with the current account deficit (twin deficits) in Indonesia and to detect the decision indicators of twin deficit as an early warning system model of twin deficits’ occurrence. The research applied a quantitative approach with granger causative data to find the significant relationship of twin deficits in Indonesia. At the early stage, it analyzes the detection of twin deficits by using quantitative phenomenological approach in a form of mathematic formula calculation via non-parametric model with EWS signal extraction. It used 1 derivation standard (DS) and 24 months signal windows to extract signal. Signal extraction is applied to monitor the evolution of economic indicators which has a systematic tendency of twin deficits in Indonesia. Microsoft Office Excel 2007 and E. Views 7 are the software used within this research. The result of this research signified that there is a relationship between twin deficits in Indonesia with the budget deficit which is influencing the current account deficit. It strengthen the Twin Deficits Hypothesis (TDH) that explains the existence of budget deficit will affect the current account deficit by access of interest rate. Additionally, the result of a signal extraction calculation from the chosen indicator variable trend showed a positive signal of twin deficits. It is justified by the abnormal behavior of variables which states up to 50 percent probability. Those variables are export growth, import growth, terms of trade, inflation, growing industrial sector, real exchange rate, foreign reserve growth, and growth of world oil price.

Spectral Error Analysis of Windowed Discrete Sinusoidal Signals

This paper presents an analysis of spectral error analysis of windowed discrete sinusoidal signals. The objective of the work is to understand how window functions affect the discrete frequency spectrum and how to minimize spectrum errors. The spectral error analysis, in which the mathematical expression of the spectral error is derived, is conducted to a discrete sinusoidal signal with single frequency. The analysis reveals the fact that the applied window functions will produce peak spectral error regardless of the sampling rate and recording time. This result has been confirmed through numerical simulations. In addition, an experiment is conducted to investigate another type of spectral error, which is frequency error. The frequency error can be minimized by collecting more samples in signal measurement process, which means the recording time is increased, even though this effort does not improve the amplitude accuracy.

Development of an HTRF Assay for the Detection and Characterization of Inhibitors of Catechol-O-Methyltransferase

Catechol-O-methyltransferase (COMT) plays an important role in the deactivation of catecholamine neurotransmitters and hormones. Inhibitors of COMT, such as tolcapone and entacapone, are used clinically in the treatment of Parkinson’s disease. Discovery of novel inhibitors has been hampered by a lack of suitable assays for high-throughput screening (HTS). Although assays using esculetin have been developed, these are affected by fluorescence, a common property of catechol-type compounds. We have therefore evaluated a new homogenous time-resolved fluorescence (HTRF)–based assay from CisBio (Codolet, France), which measures the production of S-adenosyl-L-homocysteine (SAH). The assay has been run in both HTS and medium-throughput screening (MTS) modes. The assay was established using membranes expressing human membrane-bound COMT and was optimized for protein and time to give an acceptable signal window, good potency for tolcapone, and a high degree of translation between data in fluorescence ratio and data in terms of [SAH] produced. pIC50 values for the hits from the HTS mode were determined in the MTS mode. The assay also proved suitable for kinetic studies such as Km,app determination.

A novel live cell assay to measure diacylglycerol lipase α activity.

Diacylglycerol lipase α (DAGLα) hydrolyses DAG to generate the principal endocannabinoid (eCB) 2-arachidonoylglycerol (2-AG) in the central nervous system. DAGLα dependent cannabinoid (CB) signalling has been implicated in numerous processes including axonal growth and guidance, adult neurogenesis and retrograde signalling at the synapse. Recent studies have implicated DAGLα as an emerging drug target for several conditions including pain and obesity. Activity assays are critical to the drug discovery process; however, measurement of diacylglycerol lipase (DAGL) activity using its native substrate generally involves low-throughput MS techniques. Some relatively high-throughput membrane based assays utilizing surrogate substrates have been reported, but these do not take into account the rate-limiting effects often associated with the ability of a drug to cross the cell membrane. In the present study, we report the development of a live cell assay to measure DAGLα activity. Two previously reported DAGLα surrogate substrates, p-nitrophenyl butyrate (PNPB) and 6,8-difluoro-4-methylumbelliferyl octanoate (DiFMUO), were evaluated for their ability to detect DAGLα activity in live cell assays using a human cell line stably expressing the human DAGLα transgene. Following optimization, the small molecule chromogenic substrate PNPB proved to be superior by providing lower background activity along with a larger signal window between transfected and parental cells when compared with the fluorogenic substrate DiFMUO. The assay was further validated using established DAGL inhibitors. In summary, the live cell DAGLα assay reported here offers an economical and convenient format to screen for novel inhibitors as part of drug discovery programmes and compliments previously reported high-throughput membrane based DAGL assays.

Assay and Inhibition of the Purified Catalytic Domain of Diacylglycerol Lipase Beta.

The diacylglycerol lipases (DAGLα and DAGLβ) hydrolyze DAG to generate 2-arachidonoylglycerol (2-AG), the principal endocannabinoid and main precursor of arachidonic acid (AA). The DAGLs make distinct tissue specific contributions toward 2-AG and AA levels, and therefore, selective modulators for these enzymes could play crucial roles toward harnessing their therapeutic potential. Relatively high-throughput assays have recently been reported for DAGLα and have proven useful toward the characterization of inhibitors of this enzyme. Similar assays are also warranted for DAGLβ which was the aim of this study. We first adapted previously reported DAGLα membrane assays (using PNPB and DiFMUO as substrates) to measure recombinant DAGLβ activity in membranes. In contrast to results with DAGLα, both substrates provided a relatively limited signal window for measuring DAGLβ activity, however, an improved window was obtained when employing a third commercially available substrate, EnzChek. In order to further improve on the assay parameters, we successfully purified the glutathione S-transferase (GST) tagged catalytic domain of DAGLβ. Activity of the enzyme was confirmed using EnzChek as well as two DAGL inhibitors (THL and OMDM-188). The purified DAGLβ catalytic domain assay described here provides the basis for a relatively clean and convenient assay with the potential to be adapted for high-throughput drug discovery efforts.

Accurate and fast DC offset removal method for digital relaying schemes

In digital protection systems, discrete Fourier transform (DFT) technique is the most widely used frequency analysis tool for extraction of fundamental and harmonic components of fault currents. However, some fault currents are accompanied by decaying direct current (DC) offset which affects the accuracy of the DFT-based methods as the polluted fault current with decaying DC offset is not a periodic signal. This matter may cause mal-operation of some protection schemes such as overcurrent and distance relays. In this study, to address this issue and decrease the estimation error, a novel technique is proposed to eliminate the effects of DC offset on DFT-based methods. In the proposed method, the decaying DC offset is estimated using one-cycle data windows of the fault current signal. By subtracting the estimated decaying DC offset from the fault current, the remaining signal becomes periodic. Thereafter, the DFT method can be effectively used for extraction of fundamental and other harmonic components. The performance of the proposed method is evaluated comprehensively by investigating different signals even in noisy conditions. Also, a prototype is conducted to validate the applicability of the proposed method in the real power system by real-time simulation.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Non-intrusive load monitoring based on low frequency active power measurements

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on ac- tive power signal is presented. This method works e ectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Lo eve (KL) expan- sion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible ap- pliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Pos- teriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD). The pre- sented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real households

Cluster Based Wireless Mobile Healthcare System for Physiological Data Monitoring

In recent years, importance in the medical healthcare system of Wireless Body Sensor Network (WBSN) has developed considerably. This paper discuss window based mining of vital signals, where the mining approaches are applied on the signal window in order to extract risk components and predict worst health condition. Also mining systems requires past prediction data of the patient in order to predict the future risk patterns, which is a bottleneck of the system where predicting the risk for new patient is impossible. This work also proves effective and efficient working of novel mining algorithm- ODRA (Online Distribution Resource Aware) used in healthcare.

Windowing of THz time-domain spectroscopy signals: A study based on lactose

Time-domain spectroscopy has established itself as a reference method for determining material parameters in the terahertz spectral range. This procedure requires the processing of the measured time-domain signals in order to estimate the spectral data. In this work, we present a thorough study of the properties of the signal windowing, a step previous to the parameter extraction algorithm, that permits to improve the accuracy of the results. Lactose has been used as sample material in the study.

Time-Resolved Fluorescence Assays.

Fluorescence-based detection techniques are popular in high throughput screening due to sensitivity and cost-effectiveness. Four commonly used techniques exist, each with distinct characteristics. Fluorescence intensity assays are the simplest to run, but suffer the most from signal interference. Fluorescence polarization assays show less interference from the compounds or the instrument, but require a design that results in change of fluorophore-containing moiety size and usually have narrow assay signal window. Fluorescence resonance energy transfer (FRET) is commonly used for detecting protein-protein interactions and is constrained not by the sizes of binding partners, but rather by the distance between fluorophores. Time-resolved fluorescence resonance energy transfer (TR-FRET), an advanced modification of FRET approach utilizes special fluorophores with long-lived fluorescence and earns its place near the top of fluorescent techniques list by its performance and robustness, characterized by larger assay window and minimized compound spectral interference. TR-FRET technology can be applied in biochemical or cell-based in vitro assays with ease. It is commonly used to detect modulation of protein-protein interactions and in detection of productsof biochemical reactions and cellular activities.

Ion current signal and characteristics of ethanol/gasoline dual fuel HCCI combustion

Ion current (IC) is a novel and feasible method for the diagnosis of in-cylinder combustion. The improvement of IC signal quality and reliability is always a challenge before it is applied as a sensor of engine combustion. In this paper, the characteristics of homogeneous charge compression ignition (HCCI) and IC signal with ethanol/gasoline dual fuel have been presented. The results show that increased ethanol mass fraction XEtOH retards and weakens both combustion and IC signal, with extending the phase difference (ΔθIC) between start of ion current (SOIC) and CA50 (crank angle of 50% heat release). The delayed start of gasoline injection (GSOI) enhances combustion and IC signal, but the effect is declined when XEtOH increases. This result provides a balance achieving both better quality and wider misfiring diagnosis window of IC signal. The CFD simulation results present the differences between in-cylinder local and global ion concentration with different XEtOH and GSOIs. According to the spatial distribution of electrons during the combustion, delayed GSOI causes more concentrated ionization and smaller difference between local and global start of electron appearance (SOEA).

Replication-Competent Influenza Virus and Respiratory Syncytial Virus Luciferase Reporter Strains Engineered for Co-Infections Identify Antiviral Compounds in Combination Screens.

Myxoviruses such as influenza A virus (IAV) and respiratory syncytial virus (RSV) are major human pathogens, mandating the development of novel therapeutics. To establish a high-throughput screening protocol for the simultaneous identification of pathogen- and host-targeted hit candidates against either pathogen or both, we have attempted co-infection of cells with IAV and RSV. However, viral replication kinetics were incompatible, RSV signal window was low, and an IAV-driven minireplicon reporter assay used in initial screens narrowed the host cell range and restricted the assay to single-cycle infections. To overcome these limitations, we developed an RSV strain carrying firefly luciferase fused to an innovative universal small-molecule assisted shut-off domain, which boosted assay signal window, and a hyperactive fusion protein that synchronized IAV and RSV reporter expression kinetics and suppressed the identification of RSV entry inhibitors sensitive to a recently reported RSV pan-resistance mechanism. Combined with a replication-competent recombinant IAV strain harboring nanoluciferase, the assay performed well on a human respiratory cell line and supports multicycle infections. Miniaturized to 384-well format, the protocol was validated through screening of a set of the National Institutes of Health Clinical Collection (NCC) in quadruplicate. These test screens demonstrated favorable assay parameters and reproducibility. Application to a LOPAC library of bioactive compounds in a proof-of-concept campaign detected licensed antimyxovirus therapeutics, ribavirin and the neuraminidase inhibitor zanamivir, and identified two unexpected RSV-specific hit candidates, Fenretinide and the opioid receptor antagonist BNTX-7. Hits were evaluated in direct and orthogonal dose-response counterscreens using a standard recRSV reporter strain expressing Renilla luciferase.

EEG source localization constrained by time varying fMRI.

A novel approach for Electroencephalogram (EEG) and functional Magnetic Resonance Imaging (fMRI) integration analysis was developed, specifically designed to explore the spatial and temporal details of the "sequential multi-event-related potential" type of neural activities. The approach utilizes the high temporal resolution nature of EEG to compute a current density mapping of the cortical activity, informed by the high spatial resolution fMRI in a time-variant, spatially selective manner. This method was implemented in the analysis of an EEG/fMRI study on motor activation in responses to a visual stimulus that evoked an emotional response. The processed windowed EEG signals were analyzed to select the temporally relevant partial fMRI mapping, which in turn was used to inform EEG source localization calculation. The results were compared against traditional fMRI-informed EEG approaches to demonstrate the spatiotemporal variant fMRI constraints feature as well as the performance of the developed method.

Abstract 5435: A bioluminescent assay for measuring glucose uptake

Abstract Glucose management is a key biological process. In adipose tissue and skeletal muscle, decreased rates of glucose uptake commonly associated with diabetes mellitus lead to chronic hyperglycemia and a wide array of physiological damage. In contrast, the overexpression of glucose transporters in cancer cells results in increased rates of glucose uptake to support their high rates of proliferation. Thus, activators and inhibitors of glucose uptake are useful for both diabetes management and anticancer therapies. The standard method of assaying glucose uptake involves addition of a radioactive glucose analog (2-deoxyglucose) and measurement of the accumulation of the stable and impermeable phosphorylated derivative, 2-deoxyglucose-6-phosphate (2DG6P). However, radioactive assays are laborious, costly, and require special handling of radioactive materials and waste. A simpler assay can be made by measuring the production of NADPH through the oxidation of 2DG6P by glucose-6-phosphate dehydrogenase. We have developed a bioluminescent glucose uptake assay that is both rapid and convenient and exhibits a larger signal window than comparable fluorescent or colorimetric approaches. The utility of this assay will be demonstrated with both cancer cells and insulin-sensitive 3T3L1 adipocytes and L6 myotubes. Citation Format: Michael P. Valley, Mary Sobol, Jolanta Vidugiriene, James J. Cali. A bioluminescent assay for measuring glucose uptake. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5435. doi:10.1158/1538-7445.AM2015-5435

Reproducibility in the global indices for multifocal visual evoked potentials and Humphrey visual fields in controls and glaucomatous eyes within a 2-year period.

In previous studies, we applied receiver operating characteristic curve analysis to the signal-to-noise ratio distributions in the signal and noise windows of multifocal VEP (mfVEP) response. The areas under the curve thus obtained (SNR-AUC) were found to quantitatively detect glaucomatous visual field damage. The present study evaluated the reproducibility of SNR-AUC and the Humphrey visual field (HVF) global indices in 37 eyes with primary open angle glaucoma (POAG; POAG group) and in 30 controls (control group) within a 2-year period. The HVF SITA standard 24-2 and mfVEP were recorded at three separate sessions for each individual. The intersession variability for SNR-AUC, mean deviation (MD), and pattern standard deviation (PSD) was evaluated using the repeated measures of analysis of variance and Bland-Altman plots. The logarithmically converted coefficients of variation (CV) of PSD and SNR-AUC were compared between the control and POAG groups. Linear regression analyses were performed on the logarithmic CV of SNR-AUC against the average MD, PSD, and SNR-AUC. SNR-AUC in the POAG group was significantly lower and its CV was greater compared with the control group (P < 0.0001). MD value recorded at the third visit had significantly improved than that at the first visit in the control group (analysis of variance, P = 0.03), whereas PSD value was significantly worse in the POAG group (P = 0.024). In the POAG group, SNR-AUC CV increased as the glaucoma stage became more advanced when evaluated by any functional parameters tested (i.e., MD, PSD, or SNR-AUC). The SNR-AUC of mfVEP showed a high reproducibility in control group, whereas it fluctuated more in the POAG group according to the disease severity. MD in the control group and PSD in POAG group fluctuated among sessions during the 2-year period.