Showing papers by "Sanjay Ghosh published in 2016"

Effective knockdown of Drosophila long non-coding RNAs by CRISPR interference

Abstract: Long non-coding RNAs (lncRNAs) have emerged as regulators of gene expression across metazoa. Interestingly, some lncRNAs function independently of their transcripts - the transcription of the lncRNA locus itself affects target genes. However, current methods of loss-of-function analysis are insufficient to address the role of lncRNA transcription from the transcript which has impeded analysis of their function. Using the minimal CRISPR interference (CRISPRi) system, we show that coexpression of the catalytically inactive Cas9 (dCas9) and guide RNAs targeting the endogenous roX locus in the Drosophila cells results in a robust and specific knockdown of roX1 and roX2 RNAs, thus eliminating the need for recruiting chromatin modifying proteins for effective gene silencing. Additionally, we find that the human and Drosophila codon optimized dCas9 genes are functional and show similar transcription repressive activity. Finally, we demonstrate that the minimal CRISPRi system suppresses roX transcription efficiently in vivo resulting in loss-of-function phenotype, thus validating the method for the first time in a multicelluar organism. Our analysis expands the genetic toolkit available for interrogating lncRNA function in situ and is adaptable for targeting multiple genes across model organisms.

On Fast Bilateral Filtering Using Fourier Kernels

Abstract: It was demonstrated in earlier work that, by approximating its range kernel using shiftable functions, the nonlinear bilateral filter can be computed using a series of fast convolutions. Previous approaches based on shiftable approximation have, however, been restricted to Gaussian range kernels. In this work, we propose a novel approximation that can be applied to any range kernel, provided it has a pointwise-convergent Fourier series. More specifically, we propose to approximate the Gaussian range kernel of the bilateral filter using a Fourier basis, where the coefficients of the basis are obtained by solving a series of least-squares problems. The coefficients can be efficiently computed using a recursive form of the QR decomposition. By controlling the cardinality of the Fourier basis, we can obtain a good tradeoff between the run-time and the filtering accuracy. In particular, we are able to guarantee subpixel accuracy for the overall filtering, which is not provided by the most existing methods for fast bilateral filtering. We present simulation results to demonstrate the speed and accuracy of the proposed algorithm.

Fast bilateral filtering of vector-valued images

Abstract: In this paper, we consider a natural extension of the edge-preserving bilateral filter for vector-valued images. The direct computation of this non-linear filter is slow in practice. We demonstrate how a fast algorithm can be obtained by first approximating the Gaussian kernel of the bilateral filter using raised-cosines, and then using Monte Carlo sampling. We present simulation results on color images to demonstrate the accuracy of the algorithm and the speedup over the direct implementation.

On Fast Bilateral Filtering using Fourier Kernels

Abstract: It was demonstrated in earlier work that, by approximating its range kernel using shiftable functions, the non-linear bilateral filter can be computed using a series of fast convolutions. Previous approaches based on shiftable approximation have, however, been restricted to Gaussian range kernels. In this work, we propose a novel approximation that can be applied to any range kernel, provided it has a pointwise-convergent Fourier series. More specifically, we propose to approximate the Gaussian range kernel of the bilateral filter using a Fourier basis, where the coefficients of the basis are obtained by solving a series of least-squares problems. The coefficients can be efficiently computed using a recursive form of the QR decomposition. By controlling the cardinality of the Fourier basis, we can obtain a good tradeoff between the run-time and the filtering accuracy. In particular, we are able to guarantee sub-pixel accuracy for the overall filtering, which is not provided by most existing methods for fast bilateral filtering. We present simulation results to demonstrate the speed and accuracy of the proposed algorithm.

Fast separable nonlocal means

Abstract: We propose a simple and fast algorithm called PatchLift for computing distances between patches (contiguous block of samples) extracted from a given one-dimensional signal. PatchLift is based on the observation that the patch distances can be efficiently computed from a matrix that is derived from the one-dimensional signal using lifting; importantly, the number of operations required to compute the patch distances using this approach does not scale with the patch length. We next demonstrate how PatchLift can be used for patch-based denoising of images corrupted with Gaussian noise. In particular, we propose a separable formulation of the classical nonlocal means (NLM) algorithm that can be implemented using PatchLift. We demonstrate that the PatchLift-based implementation of separable NLM is a few orders faster than standard NLM and is competitive with existing fast implementations of NLM. Moreover, its denoising performance is shown to be consistently superior to that of NLM and some of its variants, both in terms of peak signal-to-noise ratio/structural similarity index and visual quality. (C) 2016 SPIE and IS&T

A comparative assessment of temperature data from different sources for Dehradun, Uttarakhand, India

Abstract: A comparative study of extreme temperature parameters from different sources is carried out by examining standardized anomalies, trends, correlation, and equivalence of datasets. Maximum temperature (Tmax) and minimum temperature (Tmin) for Dehradun, from two different sources such as computed and gridded data from Climatic Research Unit (CRU) and observed data from India Meteorological Department (IMD) are used for 1901–2012. The CRU data are compared initially with IMD, by graphical assessment of standardized anomalies. Subsequently, change points are identified by using Cumulative Sum (CUSUM)-chart technique for trend analysis. The magnitude and significance of trends are determined by applying Sen’s slope test, and on the basis of these, trends are compared. Further, correlation analysis is carried out and datasets are tested for equivalence by using Wilcoxon–Mann–Whitney test. The result shows that annual standardized anomalies of CRU data follow the pattern of annual standardized anomalies of IMD data. The CRU data exhibit similar trends and are well correlated with IMD dataset. Moreover, CRU anomaly data are identical with IMD anomaly data in the recent decades. High resolution gridded CRU data have open access and may be more useful due to its spatio-temporal continuity for land areas of the world.

Fast and high-quality bilateral filtering using Gauss-Chebyshev approximation

Abstract: The bilateral filter is an edge-preserving smoother that has diverse applications in image processing, computer vision, computer graphics, and computational photography. The filter uses a spatial kernel along with a range kernel to perform edge-preserving smoothing. In this paper, we consider the Gaussian bilateral filter where both the kernels are Gaussian. A direct implementation of the Gaussian bilateral filter requires O(σ s 2) operations per pixel, where σ s is the standard deviation of the spatial Gaussian. In fact, it is well-known that the direct implementation is slow in practice. We present an approximation of the Gaussian bilateral filter, whereby we can cut down the number of operations to O(1) per pixel for any arbitrary σ s , and yet achieve very high-quality filtering that is almost indistinguishable from the output of the original filter. We demonstrate that the proposed approximation is few orders faster in practice compared to the direct implementation. We also demonstrate that the approximation is competitive with existing fast algorithms in terms of speed and accuracy.

Satellite Remote Sensing Technologies for Biodiversity Monitoring and Its Conservation

Abstract: Conservation of biodiversity is an essential issue due to increasing climate change and anthropological factors. Various rich biodiversity zones are greatly threatened and degrading with an alarming rate therefore it’s required to safeguard these zones and their habitats at regional and local levels. In order to implement significant conservation schemes, exhaustive information on the distribution of species on a temporal basis are required. Recently, remote sensing and biodiversity communities have started coordinating their research ideas, problems and their solutions on a single platform. The likelihood of such type of co-operations has been significantly strengthened with the advancements in satellite remote sensing technology in last decade. Thus, this advancement has empowered the interdisciplinary research at regional and local scale with high temporal resolution to provide information about changes in species distribution, habitat degradation and fine-scale disturbances of forests. This paper presents the smart satellite remote sensing technologies, which can be very useful in retrieving relevant information about biodiversity present on earth surface. This paper emphasises on various advance remote sensing imageries and their utility in deriving relevant parameters and drivers required for biodiversity monitoring. This review paper incorporates the categorization of all important and advanced sensors with respect to the essential biodiversity variables required for its monitoring and conservation.

Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach

Abstract: In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 104 for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify ...

Spectral Indices Based Change Detection in an Urban Area Using Landsat Data

Abstract: This paper proposes a technique to detect the change in some dominantly available classes in an urban area such as vegetation, built-up, and water bodies. Landsat Thematic Mapper (TM) and Landsat 8 imageries have been selected for a particular area of NCR (National Capital Region), New Delhi, India. In this study, three spectral indices have been used to characterize three foremost urban land-use classes, i.e., normalized difference built-up index (NDBI) to characterize built-up area, modified normalized difference water index (MNDWI) to signify open water and modified soil-adjusted vegetation index (MSAVI2) to symbolize green vegetation. Subsequently, for reducing the dimensionality of Landsat data, a new FCC has been generated using above mentioned indices, which consist of three thematic-oriented bands in place of the seven Landsat bands. Hence, a substantial reduction is accomplished in correlation and redundancy among raw satellite data, and consequently reduces the spectral misperception of the three land-use classes. Thus, uniqueness has been gained in the spectral signature values of the three dominant land-use classes existing in an urban area. Further, the benefits of using MSAVI2 as compared with NDVI and MNDWI as compared to NDWI for the highly urbanized area have been emphasized in this research work. Through a supervised classification, the three classes have been identified on the imageries and the change between the image pairs has been found. The overall accuracy (OA) of change detection is 92.6 %. Therefore, the study shows that this technique is effective and reliable for detection of change.

Fast and High-Quality Bilateral Filtering Using Gauss-Chebyshev Approximation

Abstract: The bilateral filter is an edge-preserving smoother that has diverse applications in image processing, computer vision, computer graphics, and computational photography The filter uses a spatial kernel along with a range kernel to perform edge-preserving smoothing In this paper, we consider the Gaussian bilateral filter where both the kernels are Gaussian A direct implementation of the Gaussian bilateral filter requires $O(\sigma_s^2)$ operations per pixel, where $\sigma_s$ is the standard deviation of the spatial Gaussian In fact, it is well-known that the direct implementation is slow in practice We present an approximation of the Gaussian bilateral filter, whereby we can cut down the number of operations to $O(1)$ per pixel for any arbitrary $\sigma_s$, and yet achieve very high-quality filtering that is almost indistinguishable from the output of the original filter We demonstrate that the proposed approximation is few orders faster in practice compared to the direct implementation We also demonstrate that the approximation is competitive with existing fast algorithms in terms of speed and accuracy

Multi-sensor Satellite Pan-sharpening based on IHS and Window Pseudo Wigner Distribution Integrated Approach: Application to WorldView-2 Imagery

Abstract: This paper proposes a new approach to fuse high spatial resolution panchromatic (PAN) image with a low spatial resolution multispectral (MS) image, based on the joint use of intensity-hue-saturation (IHS) and Window Pseudo Wigner Distribution (WPWD), with a motive to reduce the spectral distortion problem of IHS technique. This approach utilises the IHS transform to fuse high-resolution spatial information into the low-resolution MS images, and uses the WPWD to reduce the colour distortion, in the way of generating a new high-resolution PAN image that highly correlates with the intensity image of the IHS transform. The new PAN image is, then, used to replace the intensity image for an inverse IHS transform. The proposed hybrid technique has been compared with the existing hybrid techniques in terms of different fusion rules, computational time, qualitative and different quantitative parameters. The very high resolution satellite dataset, especially, WorldView-2 sensor, which provides data at subme...

Label-Free Proteomic Analysis of Flavohemoglobin Deleted Strain of Saccharomyces cerevisiae

Abstract: Yeast flavohemoglobin, YHb, encoded by the nuclear gene YHB1, has been implicated in the nitrosative stress responses in Saccharomyces cerevisiae. It is still unclear how S. cerevisiae can withstand this NO level in the absence of flavohemoglobin. To better understand the physiological function of flavohemoglobin in yeast, in the present study a label-free differential proteomics study has been carried out in wild-type and YHB1 deleted strains of S. cerevisiae grown under fermentative conditions. From the analysis, 417 proteins in Y190 and 392 proteins in ΔYHB1 were identified with high confidence. Interestingly, among the differentially expressed identified proteins, 40 proteins were found to be downregulated whereas 41 were found to be upregulated in ΔYHB1 strain of S. cerevisiae (p value < 0.05). The differentially expressed proteins were also classified according to gene ontology (GO) terms. The most enriched and significant GO terms included nitrogen compound biosynthesis, amino acid biosynthesis, translational regulation, and protein folding. Interactions of differentially expressed proteins were generated using Search Tool for the Retrieval of Interacting Genes (STRING) database. This is the first report which offers a more complete view of the proteome changes in S. cerevisiae in the absence of flavohemoglobin.

Fast Bilateral Filtering of Vector-Valued Images

Abstract: In this paper, we consider a natural extension of the edge-preserving bilateral filter for vector-valued images. The direct computation of this non-linear filter is slow in practice. We demonstrate how a fast algorithm can be obtained by first approximating the Gaussian kernel of the bilateral filter using raised-cosines, and then using Monte Carlo sampling. We present simulation results on color images to demonstrate the accuracy of the algorithm and the speedup over the direct implementation.