No abstract

Separation processes are widely used in chemical and biotechnical processes. Especially biomagnetic separation is an important issue among effective separation processes to separate the magnetic micron and submicron particles. It is necessary to establish and determine a high magnetic field or field gradient in the separation cell. However, it is not easy to determine the magnetic field gradient in the working region for different separation in practice. The reason for these difficulties is that the magnetic cells used in biochemical separation have different geometries and there are no simple and useful systems to easily measure these magnetic fields. Two main objectives are aimed in this study. First, a simple measuring device design can measure gradient magnetic fields with high precision of about 0,01mm and, secondly, obtain simple empirical expressions for the magnetic field. A magnetometer with Hall probes that works with the 3D printer principle was designed and tested to measure the magnetic field. Magnetic field changes were measured according to the surface coordinates on the measurement platform or measuring cell. Numerous experimental measurements of gradient magnetic fields generated by permanent magnets have been taken. The results obtained from the studies and results from the proposed empirical models were compared.

Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single cell level. However, scRNA-seq relies on isolating cells from tissues. Thus, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows to measure gene expression while preserving spatial information. A first step in the spatial transcriptomic analysis is to identify the cell type which requires a careful selection of cell-specific marker genes. For this purpose, currently scRNA-seq data is used to select limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a new approach for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are extracted prior to the marker gene selection step. For the selection of marker genes, cluster validity indices, Silhouette index or Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast and accurate. Even for the large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with less memory requirements.