Determination of psilocin and psilocybin using flow injection analysis with acidic potassium permanganate and tris(2,2’-bipyridyl) ruthenium(II) chemiluminescence detection respectively

A simple, rapid and sensitive method for the determination of psilocin and psilocybin is described. This is the first report on the determination of psilocin and psilocybin using flow injection analysis with acidic potassium permanganate and tris(2,2′-bipyridyl)ruthenium(II) chemiluminescence. The limits of detection (signal-to-noise ratio = 3) are 9 × 10⁻¹⁰ M and 3 × 10⁻¹⁰ M for psilocin and psilocybin, respectively.A concise synthetic route for psilocin in three steps from readily available starting materials is also described. The structures were elucidated on the basis of spectroscopic data.

Estimating expected excess returns using historical and option-implied volatility

We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward-to-risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option-implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option-implied volatility risk measure yields a positive, but statistically insignificant, risk-return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal-to-noise ratio in the latter subperiod.

Development of a new versatile computer controlled electrochemical/ESR data acquisition system

A new versatile computer controlled electrochemlcal/ESR data acquisition system has been developed for the Investigation of short-lived radicals with life-times of 20 milliseconds and greater, Different computer programs have been developed to monitor the decay of radicals; over hours or minutes, seconds or milliseconds. Signal averaging and Fourier smoothing is employed in order to improve the signal to noise ratio. Two microcomputers are used to control the system, one home-made computer containing the M6800 chip which controls the magnetic field, and an IBM PC XT which controls the electrochemistry and the data acquisition. The computer programs are written in Fortran and C, and call machine language subroutines, The system functions by having the radical generated by an electrochemical pulse: after or during the pulse the ESR data are collected. Decaying radicals which have half-lives of seconds or greater have their spectra collected in the magnetic field domain, which can be swept as fast as 200 Gauss per second. The decay of the radicals in the millisecond region is monitored by time-resolved ESR: a technique in which data is collected in both the time domain and in the magnetic field domain. Previously, time-resolved ESR has been used (without field modulation) to investigate ultra-short-lived species with life-times in the region of only a few microseconds. The application of the data acquisition system to chemical systems is illustrated. This is the first time a computer controlled system whereby the radical is generated by electrochemical means and subsequently the ESR data collected, has been developed.

Identifying co-regulating microrna groups

Background: Current miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups. Our procedure involves a sequence of statistical tests: (1) identify genes that are highly probable miRNA targets; (2) determine for each such gene, the minimum number of miRNAs that co-regulate it with high probability; (3) find, for each such gene, the combination of the determined minimum size of miRNAs that co-regulate it with the lowest p-value; and (4) discover for each such combination of miRNAs, the group of genes that are co-regulated by these miRNAs with the lowest p-value computed based on GO term annotations of the genes.
Results: Our method identifies 4, 3 and 2-term miRNA groups that co-regulate gene groups of size at least 3 in human. Our result suggests some interesting hypothesis on the functional role of several miRNAs through a "guilt by association" reasoning. For example, miR-130, miR-19 and miR-101 are known neurodegenerative diseases associated miRNAs. Our 3-term miRNA table shows that miR-130/19/101 form a co-regulating group of rank 22 (p-value =1.16 × 10^-2). Since miR-144 is co-regulating with miR-130, miR-19 and miR-101 of rank 4 (p-value = 1.16 × 10^-2) in our 4-term miRNA table, this suggests hsa-miR-144 may be neurodegenerative diseases related miRNA. Conclusions: This work identifies highly probable co-regulating miRNAs, which are refined from the prediction by computational tools using (1) signal-to-noise ratio to get high accurate regulating miRNAs for every gene, and (2) Gene Ontology to obtain functional related co-regulating miRNA groups. Our result has partly been supported by biological experiments. Based on prediction by TargetScanS, we found highly probable target gene groups in the Supplementary Information. This result might help biologists to find small set of miRNAs for genes of interest rather than huge amount of miRNA set.

Symmetrically tapered <30-micron-thick quasi-planar Ge waveguides as chemical sensors for microanalysis

Symmetrically tapered planar IR waveguides have been fabricated by starting with a ZnS coated concave piece of single-crystal Ge, embedding it in an epoxide resin as a supporting substrate, and then grinding and polishing a planar surface until the thickness at the taper minimum is <30 μm. Such tapering is expected to enhance a waveguide's sensitivity as an evanescent wave sensor by maximizing the amount of evanescent wave energy present at the thinnest part of the waveguide. As predicted by theory, the surface sensitivity, i.e., the absorbance signal per molecule in contact with the sensing region, increases with decreasing thickness of the tapered region even while the total energy throughput decreases. The signal-to-noise ratio obtained depends very strongly on the quality of the polished surfaces of the waveguides. The surface sensitivity is superior to that obtained with a commercial Ge attenuated total reflection (ATR) accessory for several types of sample, including thin films (<10 ng) and small volumes (<1 μL) of volatile solvents. By using the waveguides, light-induced structural changes in the protein bacteriorhodopsin were observable using samples as small as ∼50 pmol (∼1 μg). In addition, the waveguide sensors can reveal the surface compositions on a single human hair, pointing to their promise as a tool for forensic fiber analysis.

Image magnification using interval information

In this paper, a simple and effective image-magnification algorithm based on intervals is proposed. A low-resolution image is magnified to form a high-resolution image using a block-expanding method. Our proposed method associates each pixel with an interval obtained by a weighted aggregation of the pixels in its neighborhood. From the interval and with a linear K_α operator, we obtain the magnified image. Experimental results show that our algorithm provides a magnified image with better quality (peak signal-to-noise ratio) than several existing methods.

Design of a high sensitive double-gate field-effect transistor biosensor for DNA detection

The study of interactions between organic biomolecules and semiconducting surfaces is an important consideration for the design and fabrication of field-effect-transistor (FET) biosensor. This paper demonstrates DNA detection by employing a double-gate field effect transistor (DGFET). In addition, an investigation of sensitivity and signal to noise ratio (SNR) is carried out for different values of analyte concentration, buffer ion concentration, pH, reaction constant, etc. Sensitivity, which is indicated by the change of drain current, increases non-linearly after a specific value (∼1nM) of analyte concentration and decreases non-linearly with buffer ion concentration. However, sensitivity is linearly related to the fluidic gate voltage. The drain current has a significant effect on the positive surface group (-NH2) compared to the negative counterpart (-OH). Furthermore, the sensor has the same response at a particular value of pH (5.76) irrespective of the density of surface group, although it decreases with pH value. The signal to noise ratio is improved with higher analyte concentrations and receptor densities.

Design and analysis of a multilayer localized surface plasmon resonance graphene biosensor

This paper describes a multilayer localized surface plasmon resonance (LSPR) graphene biosensor that includes a layer of graphene sheet on top of the gold layer, and the use of different coupled configuration of a laser beam. The study also investigates the enhancement of the sensitivity and detection accuracy of the biosensor through monitoring biomolecular interactions of biotin-streptavidin with the graphene layer on the gold thin film. Additionally, the role of thin films of gold, silver, copper and aluminum in the performance of the biosensor is separately investigated for monitoring the binding of streptavidin to the biotin groups. The performance of the LSPR graphene biosensor is theoretically and numerically assessed in terms of sensitivity, adsorption efficiency, and detection accuracy under varying conditions, including the thickness of biomolecule layer, number of graphene layers and operating wavelength. Enhanced sensitivity and improved adsorption efficiency are obtained for the LSPR graphene biosensor in comparison with its conventional counterpart; however, detection accuracy under the same resonance condition is reduced by 5.2% with a single graphene sheet. This reduction in detection accuracy (signal to noise ratio) can be compensated for by introducing an additional layer of silica doped B2O3 (sdB2O3) placed under the graphene layer. The role of prism configuration, prism angle and the interface medium (air and water) is also analyzed and it is found that the LSPR graphene biosensor has better sensitivity with triangular prism, higher prism angle, lower operating wavelength and larger number of graphene layers. The approach involves a plot of a reflectivity curve as a function of the incidence angle. The outcomes of this investigation highlight the ideal functioning condition corresponding to the best design parameters.

Non-uniform sparsity in rapid compressive sensing MRI

Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This process is time-consuming and can take several minutes to acquire one image. The aim of this research is to reduce the imaging process time of MRI. This issue is addressed by reducing the number of acquired measurements using theory of Compressive Sensing (CS). Compressive Sensing exploits sparsity in MR images. Randomly under sampled k-space generates incoherent noise which can be handled using a nonlinear image reconstruction method. In this paper, a new framework is presented based on the idea to exploit non-uniform nature of sparsity in MR images, where local sparsity constrains were used instead of traditional global constraint, to further reduce the sample set. Experimental results and comparison with CS using global constraint are demonstrated.

Defining sub-regions in locally sparsified compressive sensing MRI

Magnetic Resonance Imaging (MRI) is an important imaging technique. However, it is a time consuming process. The aim of this study is to make the imaging process ef?cient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsi?ed Compressed Sensing is a specialized case of CS which sub-divides the image and sparsi?es each region separately; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for de?ning the size and locality of sub-regions in image. Experiments were done on the regions de?ned by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper.

Locally sparsified compressive sensing for improved MR image qualtity

The fact that medical images have redundant information is exploited by researchers for faster image acquisition. Sample set or number of measurements were reduced in order to achieve rapid imaging. However, due to inadequate sampling, noise artefacts are inevitable in Compressive Sensing (CS) MRI. CS utilizes the transform sparsity of MR images to regenerate images from under sampled data. Locally sparsified Compressed Sensing is an extension of simple CS. It localises sparsity constraints for sub-regions rather than using a global constraint. This paper, presents a framework to use local CS for improving image quality without increasing sampling rate or without making the acquisition process any slower. This was achieved by exploiting local constraints. Localising image into independent sub-regions allows different sampling rates within image. Energy distribution of MR images is not even and most of noise occurs due to under-sampling in high energy regions. By sampling sub-regions based on energy distribution, noise artefacts can be minimized. Experiments were done using the proposed technique. Results were compared with global CS and summarized in this paper.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

An acetylcholinesterase inhibition biosensor based on a reduced graphene oxide/silver nanocluster/chitosan nanocomposite for detection of organophosphorus pesticides

A sensitive electrochemical acetylcholinesterase (AChE) biosensor based on a reduced graphene oxide (rGO) and silver nanocluster (AgNC) modified glassy carbon electrode (GCE) was developed. rGO and AgNC nanomaterials with excellent conductivity, catalytic activity and biocompatibility offered an extremely hydrophilic surface, which facilitated the immobilization of AChE to fabricate the organophosphorus pesticide biosensor. Carboxylic chitosan (CChit) was used as a cross-linker to immobilize AChE on a rGO and AgNC modified GCE. The AChE biosensor showed favorable affinity to acetylthiocholine chloride (ATCl) and could catalyze the hydrolysis of ATCl. Based on the inhibition effect of organophosphorus pesticides on the AChE activity, using phoxim as a model compound, the inhibition effect of phoxim was proportional to its concentration ranging from 0.2 to 250 nM with a detection limit of 81 pM estimated at a signal-to-noise ratio of 3. The developed biosensor exhibited good sensitivity, stability and reproducibility, thus providing a promising tool for analysis of enzyme inhibitors and direct analysis of practical samples.