Summary. The aim of this study was to develop a flow cytometric test to quantitate low levels of circulating myeloma plasma cells, and to determine the relationship of these cells with disease stage. Cells were characterized using five-parameter flow cytometric analysis with a panel of antibodies, and results were evaluated by comparison with fluorescent consensus-primer IgH-PCR.Bone marrow myeloma plasma cells, defined by high CD38 and Syndecan-1 expression, did not express CD10, 23, 30, 34 or 45RO, and demonstrated weak expression of CD37 and CD45. 65% of patients had CD19 ¹ 56 þ plasma cells, 30% CD19 ¹ 56 low , and 5% CD19 þ 56 þ , and these two antigens discriminated myeloma from normal plasma cells, which were all CD19 þ 56 low .Peripheral blood myeloma plasma cells had the same composite phenotype, but expressed significantly lower levels of CD56 and Syndecan-1, and were detected in 75% (38/51) of patients at presentation, 92% (11/12) of patients in relapse, and 40% (4/10) of stem cell harvests. Circulating plasma cells were not detectable in patients in CR (n ¼ 9) or normals (n ¼ 10), at a sensitivity of up to 1 in 10 000 cells. There was good correlation between the flow cytometric test and IgH-PCR results: myeloma plasma cells were detectable by flow cytometry in all PCR positive samples, and samples with no detectable myeloma plasma cells were PCR negative. Absolute numbers decreased in patients responding to treatment, remained elevated in patients with refractory disease, and increased in patients undergoing relapse. We conclude that flow cytometry can provide an effective aternative to IgH-PCR that will allow quantitative assessment of low levels of residual disease.

Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.

The response of macrophages to agents such as lipopolysaccharide (LPS) and interferon (IFN) includes the transcriptional activation of numerous genes. We have used the method of differential screening of a RAW 264.7 macrophage cell line cDNA library to isolate and characterize LPS-induced messages. One such message, LRG-47, is induced by LPS, IFN-gamma, and IFN-alpha/beta, but not by a panel of other cytokines or pharmacological activating agents. LRG-47 is homologous to two other IFN-gamma-induced genes, IRG-47 and Mg21. The LRG-47 sequence is approximately 33% identical and 52% similar to both these putative protein products. All three putative proteins, particularly Mg21, bear homology to a T cell product, Tgtp, induced by T cell receptor cross-linking. The three macrophage-derived proteins share areas of homology with GTP-binding proteins, are approximately 415 amino acids in length, and have similar kinetics of induction by IFN-gamma. This suggests that these genes may be members of a new family of IFN-inducible proteins.

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