Abstract: A method of board lumber grading is performed in an industrial environment on a machine learning framework configured as an interface to a machine learning-based deep convolutional network that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics, including their sizes and boundaries.

Abstract: A method of board lumber grading is performed in an industrial environment on a machine learning framework configured as an interface to a machine learning-based deep convolutional network that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics, including their sizes and boundaries.

Abstract: A method of board lumber grading is performed in an industrial environment on a machine learning framework configured as an interface to a machine learning-based deep convolutional network that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics, including their sizes and boundaries.

Abstract: A method of board lumber (Table 2) grading is performed in an industrial environment on a machine learning framework (12) configured as an interface to a machine learning-based deep convolutional network (20) that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics (Table 1), including their sizes and boundaries.

Abstract: This disclosure provides a method of identifying or separating activated regulatory T cells that have a demethylated Foxp3 TSDR region from a mixture of activated T cells. The mixture is depleted of cells bearing the CD154 molecule (CD40 ligand). The desired cells can be recovered using a Treg marker such as CD25, GITR, CTLA4, CD137, latent TGF-beta (LAP), GARP (LRRC32) or CD121a/b. The Treg cells having a demethylated Foxp3 TSDR region can be used, for example for preparation of pharmaceutical compositions for use in treatment.

Abstract: The present invention relates to a method of identifying and separating non-regulatory T-cells (conventional T-cells) from a mixture comprising regulatory T-cells by using of the CD154 molecule (CD40 ligand) through depletion of CD154+ T-cells from the mixture or in combination with additional positive selection of Treg using markers that are specific for regulatory T-cells, such as for example, CD25, GITR, CTLA4 or markers which are specific for activated regulatory T-cells, such as, for example, CD137, “latent TGF-beta (LAP)”, GARP (LRRC32), CD121a/b, thereby generating a cell composition of activated Treg cells. The invention relates also to a kit comprising an antibody for detecting CD154 and at least one additional antibody for detecting markers for activated or non-activated regulatory T-cells. The antibodies can be coupled to a fluorescent dye or magnetic microparticles.

Abstract: The present invention relates to a method of identifying and separating non-regulatory T-cells (conventional T-cells) from a mixture comprising regulatory T-cells by using of the CD154 molecule (CD40 ligand) through depletion of CD154+ T-cells from the mixture or in combination with additional positive selection of Treg using markers that are specific for regulatory T-cells, such as for example, CD25, GITR, CTLA4 or markers which are specific for activated regulatory T-cells, such as, for example, CD137, “latent TGF-beta (LAP)”, GARP (LRRC32), CD121a/b, thereby generating a cell composition of activated Treg cells. The invention relates also to a kit comprising an antibody for detecting CD154 and at least one additional antibody for detecting markers for activated or non-activated regulatory T-cells. The antibodies can be coupled to a fluorescent dye or magnetic microparticles.