Abstract

SAM-III riboswitch, involved in regulating sulfur metabolic pathways in lactic acid bacteria, is capable of differentiating S-adenosyl-L-methionine (SAM) from its structurally similar analog S-adenosyl-L-homocysteine (SAH). Atomic level understanding of the ligand recognition mechanism of riboswitches is essential for understanding their structure-function relationships in general. In the present study, we have employed molecular dynamics (MD) simulations on five model systems to elucidate the discrimination mechanism adopted by the SAM-III riboswitch that enables differential binding of SAM with respect to SAH. The structures of the binding pocket of the riboswitch, and the modes of binding of the adenine moiety of SAM obtained from the MD simulations are similar to the experimental structure. However, MD simulations of the riboswitch-SAH complexes lead to partial unbinding of the ligand and structural changes in the RNA binding pocket. Detailed analyses were performed to examine the structural and energetic factors involved in such a differentiation. The calculations reveal a novel mechanism by which the aptamer domain specifically recognizes the adenine moiety of SAM/SAH, but SAM is better stabilized in the binding pocket due to nonspecific electrostatic interactions involving the sulfonium group. Additionally, the results support less dependence of the ligand conformation in the bound form on the effective binding of SAM to the riboswitch.

Abstract

Identification of the structural and energetic determinants responsible for enhancing the stability of proteins is crucial. Hyperthermophilic proteins are naturally occurring proteins that exhibit high thermal stability and are good candidates for the investigation and understanding of structure-stability relationships. Sac7d from Sulfolobus acidocaldarius and Sso7d from Sulfolobus solfactaricus are two homologous hyperthermophilic proteins that were shown to be quite stable at high temperatures. Molecular dynamics simulations at the nanosecond time scale at different temperatures were performed to examine the factors affecting their stability. The three-dimensional structures of these proteins were observed to be similar to the experimental structure at 300 and 360 K but were found to undergo denaturation at 500 K. Both proteins exhibit similar unfolding pathways that correlates well with the calculated intermolecular interaction energies. The differential dynamic behaviors of these molecules at different temperatures were examined. Structural and energetic analysis of the contributions of salt bridges indicates a stabilizing effect at higher temperatures. However, the lifetimes of the salt bridges were found to be quite short, and several new salt bridges formed at 500 K supporting previous studies that the desolvation penalty due to the formation of salt bridges decreases at elevated temperatures. Hydrophobic interactions, which decrease with increase in temperature, were also found to be crucial in the stability of these proteins. Overall, the study shows that a balance among the salt bridge interactions, hydrophobic interactions, and solvent properties is primarily responsible for the high thermal stability of this class of proteins.

Abstract

Motion blur is one of the most common causes of image degradation. It is of increasing interest due to the deep penetration of digital cameras into consumer applications. In this paper, we start with a hypothesis that there is sufficient information within a blurred image and approach the deblurring problem as an optimisation process where the deblurring is to be done by satisfying a set of conditions. These conditions are derived from first principles underlying the degradation process assuming noise-free environments. We propose a novel but effective method for removing motion blur from a single blurred image via an iterative algorithm. The strength of this method is that it enables deblurring without resorting to estimation of the blur kernel or blur depth. The proposed iterative method has been tested on several images with different degrees of blur. The obtained results have been compared with state of the art techniques including those that require more than one input image. The results are consistently of high quality and comparable or superior to the existing methods which demonstrates the effectiveness of the proposed technique.

Abstract

We present a novel segmentation method to better capture the boundary of a non-homogeneous object such as the optic disk(OD), defined locally by two similar characteristic regions. Existing active contour models which utilise gradient information [12] or global region intensity [2] fail to localise aforementioned boundaries. We propose a region-based active contour model that uses local image information around each point of interest in multi-dimensional feature space. This model uses a local energy functional and level-set representation to achieve desired OD segmentation. The local energy functional defined on each image point provides sufficient information to determine a desired OD segmentation which is robust to the variations found in and around the OD region. This method has been evaluated against the segmentation provided by three medical experts on 138 retinal images. Both region and boundary-based assessment performed against two well established active contour models show strengths of the proposed method.

Abstract

The aim of a local descriptor or a feature descriptor is to efficiently represent the region detected by an interest point operator in a compact format for use in various applications related to matching. The common design principle behind most of the mainstream descriptors like SIFT, GLOH, Shape context etc is to capture the spatial distribution of features using histograms computed over a grid around interest points. Histograms provide compact representation but typically loose the spatial distribution information. In this paper, we propose to use projection-based representation to improve a descriptor’s capacity to capture spatial distribution information while retaining the invariance required. Based on this proposal, two descriptors based on the CS-LBP are introduced. The descriptors have been evaluated against known descriptors on a standard dataset and found to outperform, in most cases, the existing descriptors. The obtained results demonstrate that proposed approach has the advantages of both the statistical robustness of histogram and the capability of the projection based representation to capture spatial information.

Abstract

The presence of microaneurysms (MAs) is usually an early sign of diabetic retinopathy(DR) and their automatic detection from color retinal images is of clinical interest. In this paper, we present a new approach for automatic MA detection from digital colour fundus images. We formulate MA detection as a problem of target detection from clutter, where the probability of occurrence of target is considerably smaller compared to the clutter. A successive rejection-based strategy is proposed to progressively lower the number of clutter responses. The processing stages are designed to reject specific classes of clutter while passing majority of true MAs, using a set of specialized features. The true positives that remain after the final rejector are assigned a score which is based on its similarity to a true MA. Results of extensive evaluation of the proposed approach on three different retinal image datasets is reported, and are used to highlight the promise in the presented strategy.

Abstract

Multi-Modal image registration is the primary step in fusing complementary information contained in different imaging modalities for diagnostic purposes. We focus on two specific retinal imaging modalities namely, Color Fundus Image(CFI) and Fluroscein Fundus Angiogram(FFA). In this paper we investigate a projection based method using Radon transform for accurate matching in multi-modal retinal images. A novel Radon Transform based descriptor is proposed, which is invariant to illumination, rotation and partially to scale. Our results show that our descriptor is well suited for retinal images as it is robust to lesions, and works well even in poor quality images. The descriptor has been tested on a dataset of 126 images and compared for matching application against gradient based descriptors. The results show that Radon based descriptor outperforms the gradient based ones in both being able to discriminate between true and false matches and under presence of lesions.

Abstract

Phytodiversity is affected both by natural and anthropogenic factors and in Island ecosystems these impacts can devastate or reduce diversity, if the native vegetation is lost. In addition to rich species richness and diversity, Island systems are the sites of high endemism and any threat to these ecosystems will consequently lead to loss and extinction of species. To understand the dynamics including feedbacks of these changes in phytodiversity of North Andaman Islands, a conceptual framework is proposed which focuses on understanding the land-use and land-cover changes and its impact with phytodiversity. In considering land-use and land-cover changes this work highlights the direct and indirect drivers of changes—socio-economic, biophysical and climatic factors. Migration of population, their socio economic needs and government policies were identified as major driving forces threatening the phytodiversity of these Islands. Apart from human beings, natural disasters like tsunami and introduced herbivorous animals like elephants also contributed to forest destruction in these Islands. The integrated analysis based on such framework will provide insights for holistic resource management including ecological conservation.

Abstract

Increasingly in the recent past, the focus on climate change is moving towards the understanding of the occurrence, both in magnitude and frequency, of extreme climatic events like droughts and floods. In this paper, an effort has been made towards identification of spatial co-occurrence of droughts across various subdivisions of India, based on the historical climatological data of the past century. The results show that similarities in the climate forcing factors have a role in spatio-temporal correlation of events. Also, from the results, it is seen that the well-known spatial autocorrelations measures, such as Moran Index, are not sufficient to explain such cooccurrences of events.

Abstract

We are given a set S of n points in a d-dimensional space. Given a query orthogonal box q=d i=1[ai; bi] or a semi-innite query box q=d i=1[ai;1), a One- reporting query reports YES if S q6=, else reports NO. The data structures introduced in this paper do not have their query time exponentially dependent on the dimension size, d, if the points are assumed to be randomly generated. Hence we partially break the curse of dimensionality" with which almost all the data structures for range searching sufer. The model of computation assumed is word-RAM