Morphological identification of acute leukemia is a powerful tool used by hematologists to determine the family of such a disease. In some cases, experienced physicians are even able to determine the leukemia subtype of the sample. However, the identification process may have error rates up to 40% (when classifying acute leukemia subtypes) depending on the physician’s experience and the sample quality. This problem raises the need to create automatic tools that provide hematologists with a second opinion during the classification process. Our research presents a contextual analysis methodology for the detection of acute leukemia subtypes from bone marrow cells images. We propose a cells separation algorithm to break up overlapped regions. In this phase, we achieved an average accuracy of 95% in the evaluation of the segmentation process. In a second phase, we extract descriptive features to the nucleus and cytoplasm obtained in the segmentation phase in order to classify leukemia families and subtypes. We finally created a decision algorithm that provides an automatic diagnosis for a patient. In our experiments, we achieved an overall accuracy of 92% in the supervised classification of acute leukemia families, 84% for the lymphoblastic subtypes, and 92% for the myeloblastic subtypes. Finally, we achieved accuracies of 95% in the diagnosis of leukemia families and 90% in the diagnosis of leukemia subtypes.

Libyan Desert Glass (LDG) is a natural silica-rich melted rock found as pieces scattered over the sand and bedrock of the Western Desert of Egypt, northeast of the Gilf Kebir. In this work, a population mixture analysis serves to relate the present spatial distribution of LDG mass density with the Late Oligocene-Early Miocene fluvial dynamics in the Western Desert of Egypt. This was verified from a spatial distribution model that was predicted from the log-normal kriging method using the LDG-mass-dependent transformed variable, Y(x). Both low-and high-density normal populations (-9.2 < Y(x) < -3.5 and -3.8 < Y(x) < 2.1, respectively) were identified. The low-density population was the result of an ordinary fluvial LDG transport/deposition sequence that was active from the time of the melting process, and which lasted until the end of activity of the Gilf River. The surface distribution of the high-density population allowed us to restrict the source area of the melting process. We demonstrate the importance of this geostatistical study in unveiling the probable location of the point where the melting of surficial material occurred and the role of the Gilf River in the configuration of the observed strewn field. OPEN ACCESSGeosciences 2015, 5 96

Stellar classification is an important topic in astronomical tasks such as the study of stellar populations. However, stellar classification of a region of the sky is a time-consuming process due to the large amount of objects present in an image. Therefore, automatic techniques to speed up the process are required. In this work, we study the application of a sparse representation and a dictionary learning for automatic spectral stellar classification. Our dataset consist of 529 calibrated stellar spectra of classes B to K, belonging to the Pulkovo Spectrophotometric catalog, in the 3400 − 5500Å range. These stellar spectra are used for both training and testing of the proposed methodology. The sparse technique is applied by using the greedy algorithm OMP (Orthogonal Matching Pursuit) for finding an approximated solution, and the K-SVD (K-Singular Value Decomposition) for the dictionary learning step. Thus, sparse classification is based on the recognition of the common characteristics of a particular stellar type through the construction of a trained basis. In this work, we propose a classification criterion that evaluates the results of the sparse representation techniques and determines the final classification of the spectra. This methodology demonstrates its ability to achieve levels of classification comparable

Protein purification is a complex and non-standardized process; the fact that proteins have different structural types making it difficult to create a standard methodology to obtain them in a pure, soluble, and homogeneous form. The present study shows the selective development of a buffer suitable for proteins of interest that allows high concentrations of hGPN2 protein to be obtained with low polydispersion and high homogeneity and purity. By taking the different reagents used in the construction of different buffers as a basis and performing purifications using different additives in different concentrations to determine the optimal amounts, the developed process helps to minimize the bonds, maintain solubility, release the proteins present in inclusion bodies, and provide an adequate environment for obtaining high concentrations of pure protein. GPN proteins are of unknown function, have not been purified in high concentrations, and have been found as part of the RNA polymerase assembly; if they are not expressed, the cell dies, and overexpression of certain GPN proteins has been linked to decreased survival in patients with invasive ductal carcinoma breast cancer types ER+ and HER2+. The results of the present study show that the use of the buffer developed for recombinant hGPN2 protein expressed in Escherichia coli could be manipulated in order to isolate the protein in a totally pure form and without the use of protease inhibitor tablets. The resulting homogeneity and low polydispersion was corroborated by studies carried out using dynamic dispersion analysis. Thanks to these properties, it can be used for crystallography or structural genomics studies.

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