Earticle

The paper purports to examine a mathematical model of An Ecological Ammensalism with multifarious restraints with the aid of classical method of Rk method of fourth order. The mathematical model consists of Ammensal-enemy species pair with cover for Ammensal, alternative resources for enemy and migrating for both the species.The model is characterized by a couple of first order non linear ordinary differential equations.The realtion betwenn the carrying capacity of Ammensal species and the dominance reversal time is identified.Some results are obtained from the relationship between cover protected constant of Ammensal species and the dominance reversal time.

Remote protein homology detection is a problem of detecting evolutionary relationship between proteins at low sequence similarity level. Among several problems in remote protein homology detection include the questions of determining which combination of multiple alignment and classification techniques is the best as well as the misalignment of protein sequences during the alignment process. Therefore, this paper deals with remote protein homology detection via assessing the impact of using structural information on protein multiple alignments over sequence information. This paper further presents the best combinations of multiple alignment and classification programs to be chosen. This paper also improves the quality of the multiple alignments via integration of a refinement algorithm. The framework of this paper began with datasets preparation on datasets from SCOP version 1.73, followed by multiple alignments of the protein sequences using CLUSTALW, MAFFT, ProbCons and T-Coffee for sequence-based multiple alignments and 3DCoffee, MAMMOTH-mult, MUSTANG and PROMALS3D for structural-based multiple alignments. Next, a refinement algorithm was applied on the protein sequences to reduce misalignments. Lastly, the aligned protein sequences were classified using the pHMMs generative classifier such as HMMER and SAM and also SVMs discriminative classifier such as SVM-Fold and SVM-Struct. The performances of assessed programs were evaluated using ROC, Precision and Recall tests. The result from this paper shows that the combination of refined SVM-Struct and PROMALS3D performs the best against other programs, which suggests that this combination is the best for RPHD. This paper also shows that the use of the refinement algorithm increases the performance of the multiple alignments programs by at least 4%.

Segmentation of brain tissues is one important process prior to many analyses and visualization tasks for magnetic resonance (MR) images. Clustering is one of the unsupervised techniques for doing the segmentation. Fuzzy clustering techniques have not been applied for single-channel MR images although they have shown promise in segmentation of multichannel MR images. Unfortunately, MR images always contain significant quantity of noise caused by operator performance, equipment and the environment. This noise could lead to serious inaccuracies in the segmentation result. We conduct the research in measuring the performance of fuzzy clustering algorithms over crisp clustering algorithms in different noise level for single-channel MR image. To validate the accuracy and robustness of the result of clustering algorithms we carried out experiments on simulated MR brain scans. The performance of algorithms is analyzed form three measures namely: number of iterations required, misclassification error and per class (tissue) misclassification error in different noise level present in the single-channel MR image. As, clustering is done based on some distance measure, we also compare the performance of clustering algorithms based on distance norm used for it.

The monitors in the Intensive Care Unit generate alarms whenever a signal passes beyond a preset limit. Such an approach for alarming generates many false alarms because, in general, clinically insignificant events cause signals to go beyond these limits e.g taking a blood sample. Here the alarm has been caused by a clinically insignificant event, not a disturbance in the patient's physiology. If these limits are set to the maximum allowable physiological deviation from the normal or expected value, the monitor will alarm when the patient is already in a serious condition. Likewise, if the limits are adjusted to increase sensitivity, the monitor will be more prone to giving false alarms. There is, therefore, a strong need to reduce the number of false alarms. Our approach to reducing false alarms is to use filtering techniques which will not only remove clinically insignificant events but also allow medical staff to view noise free data to enhance clinical decision support. Using real monitor data, in this paper we review a number of filtering techniques and present our findings to determine which is most suitable for the Intensive Care Unit monitors.

Cardio and cerebrocascular disorders lead to about 26 million deaths every year around the world. Cardio and cerebrovaccular disorders have not only high mortality rate across the globe but also leading to subsequent complication such as thrombolysis could favorably influence the outcome of such life-threatening disorder as myocardial infarction, cerebrovascular thrombosis, and venous thromboembolism. Our healthy system is capable to overcome to these consequences but when there is imbalance of defensive and aggressive factor in our system result come as blood clot in systemic circulation. Now role of thrombolytic agents come in picture as artificial plasminogen activators that convert plasminogen, an inactive form of plasmin to dissolve the clot by converting inactive plasminogen in active plasmin. Plasmin dissolves the fibrin blood clot, but may also degrade normal components of the hemostatic system which can further create another life threatening consequence and death also so there is always need of such an agent who specifically dissolved clot which are in circulation but not others.

Usually, bone fragility risk is related to deteriorations of osseous architecture. However, medical imaging is one of the means to appreciate in vivo bone screen, such as microscopic or micro-tomography images, which is important in the follow up of the osteoporosis. In this paper, a new image segmentation technique of trabecular bone images is introduced. It combines both hierarchical watershed segmentation, wavelet and image mosaic transform. The wavelet transform is applied to the intensity image, to de-noise the image, enhance edges in multiple resolutions, creating detail and approximation coefficients. Gradient magnitudes of the approximation image at the coarsest resolution are computed. The hierarchical watershed and the image mosaic transform are then applied to the approximation image at a given resolution. The segmented image is projected up to higher resolutions using the inverse wavelet transform. This technique provides robust segmentation results for images; reduces the watershed algorithm over-segmentation and results in closed homogeneous regions.