Preparation and characterization of magnetite (Fe3O4) nanoparticles By Sol-Gel method

The magnetite (Fe3O4) nanoparticles were successfully synthesized and annealed under vacuum at different temperature. The Fe3O4 nanoparticles prepared via sol-gel assisted method and annealed at 200-400ºC were characterized by Fourier Transformation Infrared Spectroscopy (FTIR), X-ray Diffraction spectra (XRD), Field Emission Scanning Electron Microscope (FESEM) and Atomic Force Microscopy (AFM). The XRD result indicate the presence of Fe3O4 nanoparticles, and the Scherer`s Formula calculated the mean particles size in range of 2-25 nm. The FESEM result shows that the morphologies of the particles annealed at 400ºC are more spherical and partially agglomerated, while the EDS result indicates the presence of Fe3O4 by showing Fe-O group of elements. AFM analyzed the 3D and roughness of the sample; the Fe3O4 nanoparticles have a minimum diameter of 79.04 nm, which is in agreement with FESEM result. In many cases, the synthesis of Fe3O4 nanoparticles using FeCl3 and FeCl2 has not been achieved, according to some literatures, but this research was able to obtained Fe3O4 nanoparticles base on the characterization results

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference

Copasetic analysis: a framework for the blind analysis of microarray imagery

The official published version can be found at the link below.From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePix® when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis