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Abstract To obtain the seepage evolution rule and water inrush mechanism of the collapse column, a multi-field coupled mechanical model for water inrush disasters caused by the collapse column is established in this paper, on the basis of the specific engineering conditions of the 1908 working face in the Qianjin coal mine. The mechanical model is composed of internal column elements within the collapse column and surrounding rock masses. The research focuses on the seepage evolution rule in the roof collapse column under different mining conditions and investigates the permeation instability mechanism of collapse column based on the transition of flow state. The research results indicate that the seepage pathway evolves continuously, ultimately forming a channel for water inrush, as the working face advances towards the collapse column. Besides, the water inflow increases rapidly when the working face advances 100 m, then gradually stabilizes, indicating that the seepage channel entry of the collapse column is in a stable stage. Meanwhile, mass loss in the collapse column gradually moves upward. the collapse column remains stable as a whole in the initial stage of water flow, with a small permeability, exhibiting linear flow. As time steps increases, particle loss in collapse column gradually extends to the upper part, forming a stable seepage channel. The flow velocity shows fluctuations with a slow declining trend over time.

Pediatric acute lymphoblastic leukemia (ALL) is the most common neoplasm and one of the primary causes of death in children. Its treatment is highly dependent on the correct classification of subtype. Previously, we developed a microarray-based subtype classifier based on the relative expression levels of 62 marker genes, which can predict 7 different ALL subtypes with an accuracy as high as 97% in completely independent samples. Because the classifier is based on gene expression rank values rather than actual values, the classifier enables an individualized diagnosis, without the need to reference the background distribution of the marker genes in a large number of other samples, and also enables cross platform application. Here, we demonstrate that the classifier can be extended from a microarray-based technology to a multiplex qPCR-based technology using the same set of marker genes as the advanced fragment analysis (AFA). Compared to microarray assays, the new assay system makes the convenient, low cost and individualized subtype diagnosis of pediatric ALL a reality and is clinically applicable, particularly in developing countries. [ABSTRACT FROM AUTHOR]

Pediatric acute lymphoblastic leukemia (ALL) is the most common neoplasm and one of the primary causes of death in children. Its treatment is highly dependent on the correct classification of subtype. Previously, we developed a microarray-based subtype classifier based on the relative expression levels of 62 marker genes, which can predict 7 different ALL subtypes with an accuracy as high as 97% in completely independent samples. Because the classifier is based on gene expression rank values rather than actual values, the classifier enables an individualized diagnosis, without the need to reference the background distribution of the marker genes in a large number of other samples, and also enables cross platform application. Here, we demonstrate that the classifier can be extended from a microarray-based technology to a multiplex qPCR-based technology using the same set of marker genes as the advanced fragment analysis (AFA). Compared to microarray assays, the new assay system makes the convenient, low cost and individualized subtype diagnosis of pediatric ALL a reality and is clinically applicable, particularly in developing countries. [ABSTRACT FROM AUTHOR]