Effective Quantification of Gene Expression Levels in Microarray Images Using a Spot-Adaptive Compound Clustering-Enhancement-Segmentation Scheme

In an earlier publication we reported on the effectiveness of the Burrows-Wheeler transformation followed by inversion coder (BWIC) in the lossless compression of DNA microarray images where we obtained gains of average 6.5% over generic image compressors. In this work, we propose an enhancement of our previous technique by exploiting the bit distribution of images. Using a simple statistical test, we first decide if it will be gainful to split a 16-bit microarray image into two 8-bit images. In case of splitting, it turns out that the first 8-bit image is highly compressible and we use BWIC to compress it. The second 8-bit image most often contains noise and the bit distribution can become nearly random. We use the Wald-Wolfowitz runs test of randomness to decide whether to compress the second 8-bit image with BWIC or not at all since attempting to compress random data usually results in a larger file size. On select microarray images, by splitting a 16-bit microarray image into 8-bit pieces and selectively compressing the pieces with BWIC, we can achieve upward of 3% compression gain over our previous work.

Microarray image compression: SLOCO and the effect of information loss

Quantitative real-time reverse transcription PCR (RT-qPCR) has been widely used in the detection and quantification of gene expression levels because of its high accuracy, sensitivity, and reproducibility as well as its large dynamic range. However, the reliability and accuracy of RT-qPCR depends on accurate transcript normalization using stably expressed reference genes. Amorphophallus is a perennial plant with a high content of konjac glucomannan (KGM) in its corm. This crop has been used as a food source and as a traditional medicine for thousands of years. Without adequate knowledge of gene expression profiles, there has been no report of validated reference genes in Amorphophallus. In this study, nine genes that are usually used as reference genes in other crops were selected as candidate reference genes. These putative sequences of these genes Amorphophallus were cloned by the use of degenerate primers. The expression stability of each gene was assessed in different tissues and under two abiotic stresses (heat and waterlogging) in A. albus and A. konjac. Three distinct algorithms were used to evaluate the expression stability of the candidate reference genes. The results demonstrated that EF1-a, EIF4A, H3 and UBQ were the best reference genes under heat stress in Amorphophallus. Furthermore, EF1-a, EIF4A, TUB, and RP were the best reference genes in waterlogged conditions. By comparing different tissues from all samples, we determined that EF1-α, EIF4A, and CYP were stable in these sets. In addition, the suitability of these reference genes was confirmed by validating the expression of a gene encoding the small heat shock protein SHSP, which is related to heat stress in Amorphophallus. In sum, EF1-α and EIF4A were the two best reference genes for normalizing mRNA levels in different tissues and under various stress treatments, and we suggest using one of these genes in combination with 1 or 2 reference genes associated with different biological processes to normalize gene expression. Our results will provide researchers with appropriate reference genes for further gene expression quantification using RT-qPCR in Amorphophallus.

An automated method for gridding and clustering-based segmentation of cDNA microarray images

There is genetic variability among soybean genotypes for seed quality, which may be related to seed coat characteristics. An example of this is the variable lignin content in the seed coat, a trait associated with tolerance to mechanical damage and to deterioration caused by high relative humidity. The aim of this study was to evaluate soybean seed quality and determine the relative expression of genes associated with lignin biosynthesis in soybean seed coats. Two soybean cultivars differing with respect to lignin content in the seed coat, Doko (high) and Savana (low) were studied. Evaluations were carried out on seeds of four different sizes, representing the development stages of the seeds. Seed quality was evaluated by germination, accelerated ageing and electrical conductivity tests. Through real-time PCR, relative quantification (Doko/Savana) of the genes that encode PAL, C4H, 4CL, HCT, CCoAOMT, CCR, COMT and CAD enzymes was carried out, using the beta-Actin gene as an endogenous control. According to the results of the electrical conductivity test, Doko cultivar with high lignin content has the best seed coat structure. PAL, CCoA, 4Cl and CCR genes were upregulated in Doko cultivar relative to the Savana cultivar.

Multiplex, real-time, one-step RT-PCR enables simultaneous quantification of several transcripts in one reaction vessel. This saves time and conserves precious sample material when analyzing the expression of reference and target genes in a particular sample. However, intensive PCR optimization is often required (1). We show that success in multiplex, real-time, one-step RT-PCR can be achieved at the first attempt with the QuantiTect Multiplex RT-PCR Kit, regardless of the combination of genes analyzed. In each of the multiplex assays carried out, all targets were amplified with high efficiency, which is important for precise relative quantification of gene expression levels by the deltadeltaC(T) method.

The aim of this study was to investigate the effect of aerobic training and consumption of L-carnitine supplements on HMG-CoA reductase and LDL receptor in the liver of male Wistar rats toxicated by Boldenone. 30 male Wistar rats aged 12 weeks (weight 195±7.94g) were randomly divided into five groups: control, sham, boldenone (5mg/kg), L-carnitine, aerobic training- L-carnitine.The endurance moderate intensity training program (55-50% of maximal oxygen consumption) performed for 6 weeks and 5 times a week. Injection once a week, on an appointed day, and in the quadriceps and hamstring was conducted in depth. After anesthesia, autopsy was performed and the testes Isolated. The HMG-CoA reductase and LDL receptor expression in the samples was measured by Real Time PCR and the quantification of gene expression levels using the formula 2-ΔΔct were analyzed by One-way ANOVA and post hoc Scheffe at the significant level P<0. 05. The results showed that aerobic training and supplementation with L-carnitine was significant effect on HMG CoA reductase and LDL-R in the liver of male Wistar rats toxicated by boldenone (P=0/000). The results showed that the expression of HMG-CoA reductase changes in the liver of male Wistar rats in Group training - L-carnitine significantly lower than the control group (P=0/000). Changes in the expression of LDL-R in groups training - L-carnitine and L-carnitine increased significantly compared to control group (P=0/000). According to the findings, it seems to supplementation with L-carnitine with regular aerobic training modulating the biosynthesis of cholesterol in liver tissue.

Alongside with the emergent clinical trials for Machado-Joseph disease/Spinocerebellar ataxia type 3 (MJD/SCA3) comes the need to identify molecular biomarkers of disease that can be tracked throughout the trial. MJD is an autosomal dominant neurodegenerative disorder caused by expansion of a CAG repeat in the coding region of the ATXN3 gene. Previous findings indicate the potential of transcriptional alterations in blood of MJD patients as biomarkers of disease. Accurate quantification of gene expression levels by quantitative real-time PCR (qPCR) depends on data normalization, usually performed using reference genes. Because the expression level of routinely used housekeeping genes may vary in multiple biological and experimental conditions, reference gene controls should be validated in each specific experimental design. Here, we aimed to evaluate the expression behavior of five housekeeping genes previously reported as stably expressed in peripheral blood of patients from several disorders-peptidylprolyl isomerase B (PPIB), TNF receptor associated protein 1 (TRAP1), beta-2-microglobulin (B2M), 2,4-dienoyl-CoA reductase 1 (DECR1), and folylpolyglutamate synthase (FPGS). Expression levels of these five genes were assessed by qPCR in blood from MJD subjects (preataxic and patients) and matched controls. While all housekeeping genes, here studied, were stably expressed in our sets of samples, TRAP1 showed to be the most stable gene by NormFinder and BestKeeper. We, therefore, conclude that any of these genes could be used as reference gene in future qPCR studies using blood samples from MJD subjects.

Background: Globally, head and neck cancer (HNC) considered the sixth most severe cancer associated with multiple risk factors. Due to lack of personalized therapies and undefined biomarkers, HNC shows low overall survival rate. Long non-coding RNA (LncRNA) helps to regulate multiple gene expressions in multiple biological responses. LncRNA tumor biomarkers stimulated tumor aggressiveness, cellular proliferation and inhibit apoptosis with poor prognosis. BLACAT-1, NEAT-1, HIF-1α, Caspase-3, Caspase-9 and Telomerase were associated with metastasis, tumor staging (TNM stage) activated proliferation, invasion and migration. Objective: The presented study aims to analyze tumorigenic biomarkers, (BLACAT-1, NEAT-1 and HIF-1α, Caspase-3, Caspase-9 and Telomerase) in development and progression of head and neck cancer. Meanwhile, to observe the correlation between these biomarkers in promoting head and neck cancer. Methods: In this comprehensive study, EDTA blood samples (5ml each) were collected from a cohort comprising 50 head and neck cancer (HNC) patients and an additional 30 healthy controls. The primary objective was to extract RNA from these samples for a detailed analysis. The study focused on evaluating the correlation between various tumor biomarkers across different HNC tumor subtypes. This correlation was meticulously analyzed using Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), a method known for its precision in quantifying gene expression levels. Results: BLACAT-1 shows a significant increase expression in squamous cell carcinoma. NEAT-1, Caspase-3 and Caspase-9 are non-significant in HNC tumor types whereas, inverse correlation was observed between NEAT-1 with Caspase-9 and direct proportion between NEAT-1 with Caspase-3. However, HIF-1α represented an insignificant data among all tumor subtypes under hypoxic microenvironment. Conclusion: Higher expression levels are suggesting potential biomarkers, therefore a gene signature can be developed for determining the severity of breast tumor; therefore, can be exploited as novel therapeutic approaches to block tumor metastasis and angiogenesis.

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

BackgroundMicroarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (216 - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.ResultsWe developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.ConclusionsThe presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.

A multichannel system for brain imaging containing 163 SQUID magnetometers arranged in a helmet shaped multisensorial array has been developed. To this aim, a previous investigation of a several SQUID configurations has been performed in order to choose a SQUID sensor having best performance for brain imaging on the basis of system working conditions. In particular, magnetometer and planar gradiometer have been designed, fabricated and characterized. Furthermore, a small magnetometer has been also taken into account. Since it has been decided to work in a magnetically shielding room a SQUID magnetometer has been chosen in order to guarantees high magnetic field sensitivity. These SQUID magnetometers are based on an integrated Ketchen design including a superconducting flux transformer consisting of a pickup coil and a multiturn input coil inductively coupled to the SQUID loop in a washer shape. The circuits for Flux Locked loop (FLL) operations and for Additional Positive Feedback including a thin film resistor network for gain adjusting, are integrated on the same chip containing the SQUID magnetometer. A magnetic field spectral noise as low as 1.8 fT/Hz measured in FLL operation, ensures the sensor capability to detect the tiny magnetic filed arising from brain activity. More than 200 SQUID magnetometers have been fabricated and characterized selecting the sensors with best performance. 154 measurement sensors are arranged in a SQUID multisensorial array properly designed and customized realized. Further 9 channels are located far from the scalp on three bakelite towers in order to realize via software a gradiometer to background noise rejection. The system properties have been investigated including the estimation of background noise, the shielding factor of the magnetically shielding room and the Dewar performance. Finally, preliminary measurements have been successfully performed. In particular, some brain activities such as the alpha rhythm, a spontaneous activity of a human having closed eyes and the evoked activity concerning the tapping of left and right forefinger have been analyzed. The same activities have been co-registered using the 32-channels EEG to measure bioelectric activity. The good agreement between EEG and MEG data indicates that the system operates properly.

This paper introduces a new constrained adaptive subband beamformer algorithm for speech enhancement in acoustic telecommunication systems. The solution relies on a pre-calculated source covariance matrix and recursive estimates of background noise- and handsfree signal covariance matrices. The constraint acts as an eye-opening in a vicinity of the near-field location of the source and degradations from steering-vector errors can therefor be made small. The algorithm is applied in subbands using a uniform multi channel over-sampled filterbank. Simulations with real speech recorded in an automobile hands-free environment show 19 dB noise reduction and 20 dB hands-free suppression.

The Schroeder's backward integration method is the most used method to extract the decay curve of an acoustic impulse response and to calculate the reverberation time from this curve. In the literature the limits and the possible improvements of this method are widely discussed. In this work a new method is proposed for the evaluation of the energy decay curve. The new method has been implemented in a Matlab toolbox. Its performance has been tested versus the most accredited literature method. The values of EDT and reverberation time extracted from the energy decay curves calculated with both methods have been compared in terms of the values themselves and in terms of their statistical representativeness. The main case study consists of nine Italian historical theatres in which acoustical measurements were performed. The comparison of the two extraction methods has also been applied to a critical case, i.e. the structural impulse responses of some building elements. The comparison underlines that both methods return a comparable value of the T30. Decreasing the range of evaluation, they reveal increasing differences; in particular, the main differences are in the first part of the decay, where the EDT is evaluated. This is a consequence of the fact that the new method returns a “locally defined energy decay curve, whereas the Schroeder's method accumulates energy from the tail to the beginning of the impulse response. Another characteristic of the new method for the energy decay extraction curve is its independence on the background noise estimation. Finally, a statistical analysis is performed on the T30 and EDT values calculated from the impulse responses measurements in the Italian historical theatres. The aim of this evaluation is to know whether a subset of measurements could be considered representative for a complete characterization of these opera houses.

This paper describes the VAD (voice activity detection) for controlling DTX (discontinuous transmission) of the GSM AMR (adaptive multi-rate) speech codec. The algorithm is based on spectral estimation and periodicity detection. The VAD contains a 9-band IIR filter bank, which divides input signals into frequency bands. The signal level at each band is calculated. Background noise is estimated in each sub-band. The VAD decision is computed by comparing input signal level and background noise estimate. The algorithm incorporates novel methods to estimate background noise and to detect periodic components based on open-loop pitch gain. A new method is also derived to detect correlated complex signals like music.