ORCID Profile
0000-0003-2172-7041
Current Organisation
Monash University
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Publisher: Elsevier BV
Date: 03-1982
DOI: 10.1016/0005-2760(82)90123-0
Abstract: Phospholipids from Escherichia coli K12 were converted to 1,2-diacylglycerols with phospholipase C from Bacillus cereus. High-pressure liquid chromatography of 1,2-diacylglycerol p-methoxybenzoates on LiChrosorb RP-18 using 2-propanol/acetonitrile (35:65) as eluant permitted separation of 14 molecular species. The main combinations of fatty acids were 1-16:0-2-16:1, 1-16:0-2-cyclo-17:0 and 1-16:0-2-18:1. Positional isomers were not present. The 1,2-di-16:0 compound was present at a significant level (7-10 mol%). Proportions of molecular species varied between phosphatidylethanolamine, phosphatidylglycerol and cardiolipin. Phospholipid from the outer membrane of E. coli K12 contained a lower level of molecules with two unsaturated chains than was present in the cytoplasmic membrane. The method is sensitive, has good resolving power and employs readily available equipment.
Publisher: American Chemical Society (ACS)
Date: 07-12-1982
DOI: 10.1021/BI00268A040
Abstract: The anomeric configurations of the reducing terminal glucosamine and 4-amino-4-deoxy-L-arabinose phosphates in lipopolysaccharide from Salmonella minnesota R595 have been determined by nuclear magnetic resonance. Chemical shifts for the anomeric protons were obtained by selective decoupling of the phosphorus spectrum and proton-proton coupling constants by polarization transfer from protons to phosphorus. In both cases, the phosphate is attached to the sugar in an axial orientation.
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 21-01-2021
DOI: 10.1038/S41598-021-81044-7
Abstract: Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. Furthermore, due to reasons such as ethical issues and need for human expert intervention, it is difficult to collect a large database of labelled multi-modal medical images. In addition, manual input is required to determine the fixed and moving images as input to registration algorithms. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database.
Publisher: MDPI AG
Date: 24-06-2020
DOI: 10.3390/S20123578
Abstract: Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.
Publisher: Springer Science and Business Media LLC
Date: 04-1994
DOI: 10.1007/BF00731156
Publisher: IEEE
Date: 10-2019
Publisher: Wiley
Date: 07-1985
Publisher: Microbiology Society
Date: 08-1977
Publisher: MDPI AG
Date: 11-01-2022
DOI: 10.3390/S22020523
Abstract: Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods.
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 09-2017
Publisher: IEEE
Date: 09-2017
Publisher: MDPI AG
Date: 03-04-2019
Abstract: Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for in iduals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques. First, we eliminate the background from an image to segment the region of interest (i.e., the street sign). Then, we extract the text from the segmented image and classify it. Finally, we present the identified text to the user as a voice notification. We also show through experimental results that the system performs well in real-time with a high level of accuracy. To this end, we use a database of Malaysian street sign images captured through an on-board camera.
Publisher: American Society for Microbiology
Date: 07-1994
DOI: 10.1128/JB.176.13.4144-4156.1994
Abstract: Escherichia coli K-12 has long been known not to produce an O antigen. We recently identified two independent mutations in different lineages of K-12 which had led to loss of O antigen synthesis (D. Liu and P. R. Reeves, Microbiology 140:49-57, 1994) and constructed a strain with all rfb (O antigen) genes intact which synthesized a variant of O antigen O16, giving cross-reaction with anti-O17 antibody. We determined the structure of this O antigen to be -- )-beta-D-Galf-(1-- )-alpha-D-Glcp- (1-- )-alpha-L-Rhap-(1-- )-alpha-D-GlcpNAc-(1-- , with an O-acetyl group on C-2 of the rhamnose and a side chain alpha-D-Glcp on C-6 of GlcNAc. O antigen synthesis is rfe dependent, and D-GlcpNAc is the first sugar of the biological repeat unit. We sequenced the rfb (O antigen) gene cluster and found 11 open reading frames. Four rhamnose pathway genes are identified by similarity to those of other strains, the rhamnose transferase gene is identified by assay of its product, and the identities of other genes are predicted with various degrees of confidence. We interpret earlier observations on interaction between the rfb region of Escherichia coli K-12 and those of E. coli O4 and E. coli Flexneri. All K-12 rfb genes were of low G+C content for E. coli. The rhamnose pathway genes were similar in sequence to those of (Shigella) Dysenteriae 1 and Flexneri, but the other genes showed distant or no similarity. We suggest that the K-12 gene cluster is a member of a family of rfb gene clusters, including those of Dysenteriae 1 and Flexneri, which evolved outside E. coli and was acquired by lateral gene transfer.
Publisher: PeerJ
Date: 04-03-2019
DOI: 10.7717/PEERJ-CS.181
Abstract: Three-dimensional (3D) medical image classification is useful in applications such as disease diagnosis and content-based medical image retrieval. It is a challenging task due to several reasons. First, image intensity values are vastly different depending on the image modality. Second, intensity values within the same image modality may vary depending on the imaging machine and artifacts may also be introduced in the imaging process. Third, processing 3D data requires high computational power. In recent years, significant research has been conducted in the field of 3D medical image classification. However, most of these make assumptions about patient orientation and imaging direction to simplify the problem and/or work with the full 3D images. As such, they perform poorly when these assumptions are not met. In this paper, we propose a method of classification for 3D organ images that is rotation and translation invariant. To this end, we extract a representative two-dimensional (2D) slice along the plane of best symmetry from the 3D image. We then use this slice to represent the 3D image and use a 20-layer deep convolutional neural network (DCNN) to perform the classification task. We show experimentally, using multi-modal data, that our method is comparable to existing methods when the assumptions of patient orientation and viewing direction are met. Notably, it shows similarly high accuracy even when these assumptions are violated, where other methods fail. We also explore how this method can be used with other DCNN models as well as conventional classification approaches.
Publisher: Oxford University Press (OUP)
Date: 1994
Abstract: kappa-Casein is the major glycoprotein in bovine milk. It has a proteinase-sensitive (chymosin) site which cleaves the glycoprotein into two segments: N-terminal para-kappa-casein domain and the C-terminal kappa-casein macroglycopeptide domain which is highly heterogeneous in oligosaccharide content. We have identified six sites of O-glycosylation on the macroglycopeptide by solid-phase Edman degradation: Thr121, Thr131, Thr133, Thr136 (A variant only), Thr142 and Thr165. No Ser residues are glycosylated. The glycosylation status of 15 of 17 potential O-glycosylation sites in the B variant was accurately predicted using the four peptide motifis previously proposed for the glycosylation of human glycophorin A (Pisano, A., Redmond, J.W., Williams, K.L. and Gooley, A.A., Glycobiology, 3, 429-435, 1993), provided one additional assumption is made concerning an inhibitory role for a nearby Ile.
Publisher: MDPI AG
Date: 13-04-2017
DOI: 10.3390/S17040853
Publisher: Springer Science and Business Media LLC
Date: 1990
DOI: 10.1007/BF00030067
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 1985
DOI: 10.1007/BF00398091
Publisher: IEEE
Date: 12-2017
Publisher: Elsevier BV
Date: 12-1985
Publisher: Elsevier BV
Date: 12-1985
Publisher: IEEE
Date: 06-2019
Publisher: MDPI AG
Date: 30-07-2017
DOI: 10.3390/SYM9080138
Abstract: The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the well-maintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.
Publisher: Elsevier BV
Date: 10-1980
DOI: 10.1016/S0021-9673(00)80524-5
Abstract: Inflammatory contributions from diet and adiposity may interact with respect to the development of type 2 diabetes mellitus (T2DM). We investigated the degree to which adiposity modified the association between dietary inflammatory potential and incident T2DM. Data from 6,016 US men in the Aerobics Center Longitudinal Study who completed a 3-day diet record were used. The inflammatory potential of diet was characterized by the Dietary Inflammatory Index (DII®), and adiposity was assessed with body mass index, waist circumference, body fat percentage (BF) and waist-to-height ratio. Inverse probability weights were used in modified Poisson regression models to examine whether adiposity modifies the relationship between the DII and T2DM, while accounting for selection bias from participants who were lost to follow-up. There were 336 incident cases of T2DM after a mean follow-up of 6.5 years. DII scores were not significantly associated with T2DM incidence in multivariable models, but point estimates were consistently elevated across increasing DII quartiles compared to the most anti-inflammatory DII quartile. In the model that evaluated BF, the term for overall effect modification was significant (p = 0.02), but there was no evidence of effect modification on the multiplicative and additive scales when examined further. Effect modification was not present for any other adiposity measures. We did not observe evidence that a pro-inflammatory diet, as measured by the DII, is associated with incidence of T2DM, nor evidence that adiposity modifies a potential relationship. Further investigation is needed in larger cohorts with longer follow-up.
Publisher: Microbiology Society
Date: 08-1978
Publisher: Springer Science and Business Media LLC
Date: 06-1994
DOI: 10.1007/BF00731216
Publisher: Oxford University Press (OUP)
Date: 10-1981
Publisher: Wiley
Date: 10-1986
Publisher: Springer US
Date: 1995
No related grants have been discovered for Kh Tohidul ISLAM.