Sparse coding for improved signal-to-noise ratio in MRI

Magnetic Resonance images (MRI) do not only exhibit sparsity but their sparsity take a certain predictable shape which is common for all kinds of images. That region based localised sparsity can be used to de-noise MR images from random thermal noise. This paper present a simple framework to exploit sparsity of MR images for image de-noising. As, noise in MR images tends to change its shape based on contrast level and signal itself, the proposed method is independent of noise shape and type and it can be used in combination with other methods.

Application of power functions to chromatographic data for the enhancement of signal to noise ratios and separation resolution

Chromatographic detection responses are recorded digitally. A peak is represented ideally by a Guassian distribution. Raising a Guassian distribution to the power ‘n’ increases the height of the peak to that power, but decreases the standard deviation by √n. Hence there is an increasing disparity in detection responses as the signal moves from low level noise, with a corresponding decrease in peak width. This increases the S/N ratio and increases peak to peak resolution. The ramifications of these factors are that poor resolution in complex chromatographic data can be improved, and low signal responses embedded at near noise levels can be enhanced. The application of this data treatment process is potentially very useful in 2D-HPLC where sample dilution occurs between dimension, reducing signal response, and in the application of post-reaction detection methods, where band broadening is increased by virtue of reaction coils. In this work power functions applied to chromatographic data are discussed in the context of (a) complex separation problems, (b) 2D-HPLC separations, and (c) post-column reaction detectors.

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.

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.

Stabilizing sparse cox model using statistic and semantic structures in electronic medical records

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures – the Consistency index and signal-to-noise ratio (SNR) – the use of our proposed methods significantly increased feature stability when compared with the baselines.

Fourier transform infrared microspectroscopy as a tool to identify macroalgal propagules

Understanding of macroalgal dispersal has been hindered by the difficulty in identifying propagules. Different carrageenans typically occur in gametophytes and tetrasporophytes of the red algal family Gigartinaceae, and we may expect that carpospores and tetraspores also differ in composition of carrageenans. Using Fourier transform infrared (FT-IR) microspectroscopy, we tested the model that differences in carrageenans and other cellular constituents between nuclear phases should allow us to discriminate carpospores and tetraspores of Chondrus verrucosus Mikami. Spectral data suggest that carposporophytes isolated from the pericarp and female gametophytes contained κ-carrageenan, whereas tetrasporophytes contained λ-carrageenan. However, both carpospores and tetraspores exhibited absorbances in wave bands characteristic of κ-,ι-, and λ-carrageenans. Carpospores contained more proteins and may be more photosynthetically active than tetraspores, which contained more lipid reserves. We draw analogies to planktotrophic and lecithotrophic larvae. These differences in cellular chemistry allowed reliable discrimination of spores, but pretreatment of spectral data affected the accuracy of classification. The best classification of spores was achieved with extended multiplicative signal correction (EMSC) pretreatment using partial least squares discrimination analysis, with correct classification of 86% of carpospores and 83% of tetraspores. Classification may be further improved by using synchrotron FT-IR microspectroscopy because of its inherently higher signal-to-noise ratio compared with microspectroscopy using conventional sources of IR. This study demonstrates that FT-IR microspectroscopy and bioinformatics are useful tools to advance our understanding of algal dispersal ecology through discrimination of morphologically similar propagules both within and potentially between species.

Neuron’s spikes noise level classification using hidden markov models

Considering that the uncertainty noise produced the decline in the quality of collected neural signal, this paper proposes a signal quality assessment method for neural signal. The method makes an automated measure to detect the noise levels in neural signal. Hidden Markov Models were used to build a classification model that classifies the neural spikes based on the noise level associated with the signal. This neural quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information.

Noise level classification for EEG using Hidden Markov Models

EEG signal is one of the most important signals for diagnosing some diseases. EEG is always recorded with an amount of noise, the more noise is recorded the less quality is the EEG signal. The included noise can represent the quality of the recorded EEG signal, this paper proposes a signal quality assessment method for EEG signal. The method generates an automated measure to detect the noise level of the recorded EEG signal. Mel-Frequency Cepstrum Coefficient is used to represent the signals. Hidden Markov Models were used to build a classification model that classifies the EEG signals based on the noise level associated with the signal. This EEG quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information. Moreover, our model was applied on an uncontrolled environment and on controlled environment and a result comparison was applied.

MMSE precoder for unitary space–time codes in correlated time-varying channels

This letter addresses the problem of the design of a precoder for multiple transmit antenna communication systems with spatially and temporally correlated fading channels. By using the asymptotic (high signal-to-noise ratio) mean-square error of the channel estimates, the letter derives a precoder for unitary space-time codes that can exploit the spatiotemporal correlation in the time-varying fading channels. Simulation results illustrate that significant performance gains can be achieved by using the new precoder.

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.