Prediction of relative solvent accessibility by support vector regression and best-first method

Since, it is believed that the native structure of most proteins is defined by their sequences, utilizing data mining methods to extract hidden knowledge from protein sequences, are unavoidable. A major difficulty in mining bioinformatics data is due to the size of the datasets which contain frequently large numbers of variables. In this study, a two-step procedure for prediction of relative solvent accessibility of proteins is presented. In a first “feature selection” step, a small subset of evolutionary information is identified on the basis of selected physicochemical properties. In the second step, support vector regression is used to real value prediction of protein solvent accessibility with these custom selected features of evolutionary information. The experiment results show that the proposed method is an improvement in average prediction accuracy and training time

Algebraic, Topological, and Geometric Driven Convolutional Neural Networks for Ultrasound Imaging Cancer Diagnosis

Despite the astonishing successes of Convolutional Neural Networks (CNN) as a powerful deep learning tool for a variety of computer vision tasks, their deployments for ultrasound (US) tumour image analysis within clinical settings is challenging due to the difficulty of interpreting CNN decisions compounded by lack of availability of class labelled “good quality” US tumour image datasets that represent an i.i.d random sample of the unknown population. The use of CNN models pretrained on natural images in transfer learning (TL) mode for US image analysis are perceived to suffer from a lack of robustness to small changes and inability to generalisation to unseen data. This thesis aims to develop a strategy for designing efficient CNN architectures customised for US images that overcome or significantly reduce the above challenges while learning discriminating features resulting in highly accurate diagnostic predictions. We first uncover the significant differences in the statistical contents and spatial distribution of image texture landmarks (e.g. Local Binary Patterns) between US images and natural images. Therefore, we investigate the effects of convolution with random Gaussian filters (RGF) on US image content in terms of spatial and an innovative texture-based entropy, and the spatial distribution of texture landmarks. These effects are determined for US scan images of malignant and benign masses for breast, bladder, and liver tissues. We demonstrate that several pretrained CNN models retrained on US tumour scan images in TL mode achieve high diagnostic accuracy but suffer greatly from a lack of robustness against natural data perturbation and significantly low generalisation rates due to highly ill-conditioned convolutional layer filters. Thus,we investigate the behaviour of the CNN models during the training process in terms of three mathematically linked characterisation of the filters point clouds: (1) the distribution of their condition numbers, (2) their spatial distribution using persistent homology (PH) tools, and (3) their effects on tumour discriminating power of texture landmark PH scheme in convolved images. These results pave the way for a credible strategy to develop high-performing customised CNN architectures that are robust and generalise well to unseen US scans. We further develop a newapproach to ensure equal condition numbers across the different channel wise filters at initialisation, andwe highlight their impact on the PH profiles as point clouds. However, the condition number of filters continues to be unstable during training, therefore we introduce a simple novel matrix surgery procedure depending on singular value decomposition as a spectral regularisation. We illustrate that the PH of different point clouds of RGFs and their inverses are distinct (in terms of their birth/death of connected components and holes in dimensions 0 and 1) depending on variation in their condition number distributions. This behaviour changes as a result of applying SVD-surgery, so that the PH of point cloud of a filter set post SVD-surgery approaches the same shape and connectivity of a point cloud of orthogonal RGFs

Capacity Building for Primary Stroke Prevention Teams in Children Living With Sickle Cell Anemia in Africa

Background: Nigeria has the highest proportion of children with sickle cell anemia (SCA) globally; an estimated 150,000 infants with SCA are born annually. Primary stroke prevention in children with SCA must include Nigeria. We describe capacity-building strategies in conjunction with two National Institutes of Health–funded primary stroke prevention trials (a feasibility trial and phase III randomized controlled trial) with initial hydroxyurea treatment for children with SCA and abnormal transcranial Doppler (TCD) velocities in Nigeria. We anticipated challenges to conducting clinical trials in a low-resource setting with a local team that had not previously been involved in clinical research and sought a sustainable strategy for primary stroke prevention. Methods: This is a descriptive, prospective study of challenges, solutions, and research teams in two trials that enrolled a total of 679 children with SCA. Results: As part of the capacity-building component of the trials, over eight years, 23 research personnel (physicians, nurses, research coordinators, a statistician, and a pharmacist) completed a one-month research governance and ethics training program at Vanderbilt University Medical Center, USA. A lead research coordinator for each site completed the Society of Clinical Research Professionals certification. TCD machines were donated; radiologists and nonradiologists were trained and certified to perform TCD. A scalable E-prescription was implemented to track hydroxyurea treatment. We worked with regional government officials to support ongoing TCD-based screening and funding for hydroxyurea for children with SCA at a high risk of stroke. Conclusions: Our trials and capacity building demonstrate a sustainable strategy to initiate and maintain pediatric SCA primary stroke prevention programs in Africa

Synthesis and Effects of 4,5-Diaryl-2-(2-alkylthio-5-imidazolyl) Imidazoles as Selective Cyclooxygenase Inhibitors

Objective(s)In recent years highly selective COX-2inhibitors were withdrawn from the market because of an increased risk of cardiovascular complications. In this study we were looking for potent compounds with moderate selectivity for cox-2. So, four analogues of 4, 5-diaryl-2-(2-alkylthio-5-imidazolyl) imidazole derivatives were synthesized and their anti-inflammatory and anti-nociceptive activities were evaluated on male BALB/c mice (25-30 g). Molecular modeling and in vitro COX-1 and COX-2 isozyme inhibition studies were also performed. Materials and Methods2-(2-Alkylthio-5-imidazolyl)-4,5-diphenylimidazole compounds were obtained by the reaction of benzyl with 2-alkylthio-1-benzylimidazole-5-carbaldehyde, in the presence of ammonium acetate. Spectroscopic data and elemental analysis of compounds were obtained and their structures elucidated. Anti-nociception effects were examined using writhing test in mice. The effect of the analogues (7.5, 30, 52.5 and 75 mg/kg) against acute inflammation were studied using xylene-induced ear edema test in mice. Celecoxib (75 mg/kg) was used as positive control.ResultsAll four analogues exhibited anti-nociceptive activity against acetic acid induced writhing, but did not show significant analgesic effect (P< 0.05) compared with celecoxib. It was shown that analogues injected 30 min before xylene application reduced the weight of edematic ears. All analogues were found to have less selectivity for COX-2 in comparison to celecoxib. ConclusionInjected doses of synthesised analogues possesses favorite anti-nociceptive effect and also has anti-inflammatory effects, but comparing with celecoxib this effect is not significantly different. On the other hand selectivity index for analogues is less than celecoxib and so we expect less cardiovascular side effects for these compounds

Fast and green synthesis of biologically important quinoxalines with high yields in water

Optimal method were developed for the green synthesis of quinoxaline derivatives based on the highly efficient and simple condensation reaction of various aromatic 1,2-diketones and 1,2-diamines in nearly quantitative yields in water. In this method we did not use any catalyst. The very mild reaction conditions, the high yields of the products, and the absence of any catalyst make this methodology an efficient and green route for synthesis of quinoxalines

Transition metal-free oxidation of benzylic alcohols to carbonyl compounds by hydrogen peroxide in the presence of acidic silica gel

Oxidation of alcohols to carbonyl compounds has become an important issue in the process industry as well as many other applications. In this method, various benzylic alcohols were successfully converted to corresponding aldehydes and ketones under transition metal-free condition using hydrogen peroxide in the presence of some amount of catalytic acidic silica gel. Silica gel is inexpensive and available. One of the most important features of this method is its short reaction time